Genetic Neural Architecture Search for automatic assessment of human sperm images

Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automa...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Expert systems with applications Ročník 188; s. 115937
Hlavní autoři:	Miahi, Erfan, Mirroshandel, Seyed Abolghasem, Nasr, Alexis
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 01.02.2022 Elsevier BV Elsevier
Témata:	Artificial Intelligence Artificial neural networks Bioinformatics Computer architecture Computer Science Datasets Deep learning Evaluation Fitness Genetic algorithm Genetic algorithms Human sperm morphometry Infertility Machine learning Medical imaging Morphology Neural architecture search Neural networks Search algorithms Search methods Sperm Deep learning Neural architecture search Human sperm morphometry Genetic algorithm Infertility Human Sperm Morphometry Neural Architecture Search Deep Learning Genetic Algorithm
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results. •A novel neural architecture search based on Genetic Algorithm.•A novel neural architecture encoding for exploration of search space.•A specific Crossover and Mutation operations for neural architecture search.•Work on imbalanced datasets, low resolution images, and non-stained sperms.•State-of-the-art accuracy & Real-time sperm morphological analysis.
AbstractList	Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results. •A novel neural architecture search based on Genetic Algorithm.•A novel neural architecture encoding for exploration of search space.•A specific Crossover and Mutation operations for neural architecture search.•Work on imbalanced datasets, low resolution images, and non-stained sperms.•State-of-the-art accuracy & Real-time sperm morphological analysis. Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, nonreproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1, 540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results. Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results.
ArticleNumber	115937
Author	Nasr, Alexis Mirroshandel, Seyed Abolghasem Miahi, Erfan
Author_xml	– sequence: 1 givenname: Erfan surname: Miahi fullname: Miahi, Erfan email: mhi.erfan1@gmail.com organization: Department of Computer Engineering, University of Guilan, Rasht, Iran – sequence: 2 givenname: Seyed Abolghasem orcidid: 0000-0001-8853-9112 surname: Mirroshandel fullname: Mirroshandel, Seyed Abolghasem email: mirroshandel@guilan.ac.ir organization: Department of Computer Engineering, University of Guilan, Rasht, Iran – sequence: 3 givenname: Alexis surname: Nasr fullname: Nasr, Alexis email: alexis.nasr@univ-amu.fr organization: Laboratoire dÍnformatique et Systemes, Aix Marseille Université, Marseille, France
BackLink	https://hal.science/hal-03585035$$DView record in HAL
BookMark	eNp9kU1P7CAUhonRxPHjD7giceWic4HSQhM3E-NXMrnGqGvClIPDZFpGoHPjv5emurkLN5xA3gfOeThBh73vAaELSuaU0PrPZg7xn54zwuic0qopxQGaUSnKohZNeYhmpKlEwangx-gkxg0hVBAiZuj5HnpIrsV_YQh6ixehXbsEbRoC4BfQeYutD1gPyXd6DOoYIcYO-oS9xeuh0z2OOwgddp1-h3iGjqzeRjj_rqfo7e729eahWD7dP94slkXLBUtFY1YrCVYIKwxQ0hguLbe84mLVaMYNyNpIxumqqjm1klAjDWXcGmIFcGDlKbqa7l3rrdqF_Hj4VF479bBYqvGMlJWs8rKnOXs5ZXfBfwwQk9r4IfS5PcVqInlT11WZU3JKtcHHGMCq1qU8s-9T0G6rKFGjbLVRo2w1ylaT7Iyy_9Cfjn6FricIsqa9g6Bi66BvwbiQf0AZ737DvwA89Jn6
CitedBy_id	crossref_primary_10_1016_j_bspc_2024_106152 crossref_primary_10_3390_math11030515 crossref_primary_10_1016_j_compmedimag_2024_102441 crossref_primary_10_1080_21681163_2024_2347978 crossref_primary_10_1109_TAI_2024_3477457 crossref_primary_10_3390_diagnostics13061100 crossref_primary_10_3390_electronics14091820 crossref_primary_10_1016_j_engfailanal_2024_108426 crossref_primary_10_32604_cmes_2023_030391 crossref_primary_10_3390_healthcare12070781 crossref_primary_10_1111_rda_70024 crossref_primary_10_3390_genes14020451 crossref_primary_10_1016_j_asoc_2023_110412 crossref_primary_10_1145_3603704 crossref_primary_10_1016_j_imu_2024_101565 crossref_primary_10_1016_j_neucom_2024_127664 crossref_primary_10_1016_j_apm_2023_10_025
Cites_doi	10.1016/j.anireprosci.2013.04.002 10.1109/JPROC.2015.2494218 10.1016/j.compbiomed.2019.04.030 10.1109/5.726791 10.1016/j.cmpb.2015.08.013 10.1016/j.bspc.2017.11.009 10.26415/2572-004X-vol2iss4p301-307 10.5120/743-1050 10.1155/2018/7068349 10.1038/s42003-019-0491-6 10.1109/CVPR.2009.5206848 10.1016/j.compbiomed.2017.03.004 10.1016/j.theriogenology.2011.10.039 10.1609/aaai.v33i01.33014780 10.1002/j.1939-4640.2004.tb02807.x 10.1016/j.compbiomed.2019.103342 10.1038/s41591-018-0316-z 10.1109/COGANN.1992.273950 10.1016/j.compbiomed.2008.01.005 10.1016/j.contraception.2005.05.007 10.4236/jbise.2012.57049 10.1109/TSMC.1979.4310076 10.1016/j.fertnstert.2007.08.071 10.1162/106365602320169811 10.1109/CVPR.2018.00257 10.1002/for.3980040103 10.1007/978-3-030-05318-5_3 10.1093/humrep/deq075 10.1016/j.cmpb.2014.06.018
ContentType	Journal Article
Copyright	2021 Elsevier Ltd Copyright Elsevier BV Feb 2022 Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: 2021 Elsevier Ltd – notice: Copyright Elsevier BV Feb 2022 – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D 1XC
DOI	10.1016/j.eswa.2021.115937
DatabaseName	CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Hyper Article en Ligne (HAL)
DatabaseTitle	CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1873-6793
ExternalDocumentID	oai:HAL:hal-03585035v1 10_1016_j_eswa_2021_115937 S0957417421012914
GroupedDBID	--K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABBOA ABFNM ABKBG ABMAC ABMVD ABUCO ABXDB ABYKQ ACDAQ ACGFS ACHRH ACNNM ACNTT ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SET SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 8FD AFXIZ AGCQF AGRNS BNPGV JQ2 L7M L~C L~D SSH 1XC
ID	FETCH-LOGICAL-c472t-9dbb8ef77f7de109d48f4f4547b9a24de86d8241b5641f801d8d124fd0f7e4e23
ISICitedReferencesCount	22
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000768193500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0957-4174
IngestDate	Tue Oct 14 20:32:03 EDT 2025 Sun Jul 13 04:41:18 EDT 2025 Sat Nov 29 07:10:08 EST 2025 Tue Nov 18 19:52:40 EST 2025 Fri Feb 23 02:42:39 EST 2024
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Deep learning Neural architecture search Human sperm morphometry Genetic algorithm Infertility Human Sperm Morphometry Neural Architecture Search Deep Learning Genetic Algorithm
Language	English
License	Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c472t-9dbb8ef77f7de109d48f4f4547b9a24de86d8241b5641f801d8d124fd0f7e4e23
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8853-9112
OpenAccessLink	https://doi.org/10.48550/arxiv.1909.09432
PQID	2608496653
PQPubID	2045477
ParticipantIDs	hal_primary_oai_HAL_hal_03585035v1 proquest_journals_2608496653 crossref_citationtrail_10_1016_j_eswa_2021_115937 crossref_primary_10_1016_j_eswa_2021_115937 elsevier_sciencedirect_doi_10_1016_j_eswa_2021_115937
PublicationCentury	2000
PublicationDate	2022-02-01
PublicationDateYYYYMMDD	2022-02-01
PublicationDate_xml	– month: 02 year: 2022 text: 2022-02-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	Expert systems with applications
PublicationYear	2022
Publisher	Elsevier Ltd Elsevier BV Elsevier
Publisher_xml	– name: Elsevier Ltd – name: Elsevier BV – name: Elsevier
References	Zoph, Le (b69) 2016 Ghasemian, Mirroshandel, Monji-Azad, Azarnia, Zahiri (b18) 2015; 122 Kotikalapudi (b30) 2017 Abràmoff, Magalhães, Ram (b3) 2004; 11 Suganuma, Shirakawa, Nagao (b60) 2017 Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998. Gradient-based learning applied to document recognition. In Elsken, T., Metzen, J. H., & Hutter, F. 2019. Efficient multi-objective neural architecture search via Lamarckian evolution. In Vicente-Fiel, Palacin, Santolaria, Yániz (b62) 2013; 139 Meyer-Lee, Uppili, Zhao (b41) 2017 Mitchell (b43) 1998 He, Zhao, Chu (b20) 2019 Maree, Du Plessis, Menkveld, Van der Horst (b39) 2010; 25 Riordon, McCallum, Sinton (b49) 2019 Alegre, Biehl, Petkov, Sánchez (b4) 2008; 38 LeCun, Bottou, Orr, Müller (b35) 2012 Xie, Yuille (b64) 2017 Chang, Saavedra, Castañeda, Sarabia, Hitschfeld, Härtel (b11) 2014; 117 Yániz, Vicente-Fiel, Capistrós, Palacín, Santolaria (b65) 2012; 77 Elsken, Metzen, Hutter (b14) 2018 Baker, Gupta, Raskar, Naik (b6) 2017 Ramos, de Boer, Meuleman, Braat, Wetzels (b46) 2004; 25 Pham, Guan, Zoph, Le, Dean (b45) 2018 Boumaza, Loukil, Aarizou (b8) 2018; 2 Krizhevsky, Sutskever, Hinton (b33) 2012 Sanchez, Petkov, Alegre (b51) 2006; 52 Shahriari, Swersky, Wang, Adams, De Freitas (b54) 2016; 104 (pp. 2171–2180). (pp. 248–255). McCallum, Riordon, Wang, Kong, You, Sanner (b40) 2019; 2 Zoph, Vasudevan, Shlens, Le (b70) 2017 . Kingma, Ba (b26) 2014 Baker, Gupta, Naik, Raskar (b5) 2017 Ilhan, Aydin (b21) 2018; 41 Stanley, Miikkulainen (b58) 2002; 10 Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019). Regularized evolution for image classifier architecture search. In Abadi, Barham, Chen, Chen, Davis, Dean (b1) 2016; 16 Liu, H., Simonyan, K., & Yang, Y. (2091). DARTS: Differentiable architecture search. In Miller, Todd, Hegde (b42) 1989; 89 Schaffer, J. D., Whitley, D., & Eshelman, L. J. (1992). Combinations of genetic algorithms and neural networks: A survey of the state of the art. In Chollet (b12) 2015 Zeiler, Fergus (b66) 2013 Jin, Song, Hu (b24) 2018 Javadi, Mirroshandel (b23) 2019; 109 Kitano (b27) 1990; 4 Krizhevsky, Sutskever, Hinton (b32) 2012 IEEE (pp. 1–37). Esteva, Robicquet, Ramsundar, Kuleshov, DePristo, Chou (b16) 2019; 25 Klambauer, Unterthiner, Mayr, Hochreiter (b28) 2017 Krizhevsky, Hinton (b31) 2009 Abbiramy, Shanthi (b2) 2010; 3 Li, Tseng, Dong, Li, Zhao, Ding (b36) 2014 Sánchez, Petkov, Alegre (b50) 2005 Bijar, Benavent, Mikaeili (b7) 2012; 5 URL Zhang (b67) 2017 Liu, Simonyan, Vinyals, Fernando, Kavukcuoglu (b37) 2017 Otsu (b44) 1979; 9 Haugen, Andersen, Witczak, Hammer, Hicks, Borgli (b19) 2019 Selvaraju, Das, Vedantam, Cogswell, Parikh, Batra (b53) 2016 Real, Moore, Selle, Saxena, Suematsu, Tan (b48) 2017 (pp. 4780–4789). (pp. 2278–2324). Gardner Jr (b17) 1985; 4 Snoek, Larochelle, Adams (b56) 2012 Chang, Garcia, Hitschfeld, Härtel (b10) 2017; 83 Voulodimos, Doulamis, Doulamis, Protopapadakis (b63) 2018; 2018 Szegedy, Ioffe, Vanhoucke (b61) 2016 Kandasamy, Neiswanger, Schneider, Poczos, Xing (b25) 2018 Klein, Falkner, Bartels, Hennig, Hutter (b29) 2017; vol. 54 Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., & Sundaram, N., et al. (2015). Scalable bayesian optimization using deep neural networks. In Stouffs, Tournaye, Van der Elst, Liebaers, Lissens (b59) 2008; 90 Deng, J., Dong, W., Socher, R., & and, L. L. 2009. ImageNet: A large-scale hierarchical image database. In (pp. 2423–2432). Cai, Chen, Zhang, Yu, Wang (b9) 2018 Isidori, Latini, Romanelli (b22) 2005; 72 Shaker (b55) 2017 Zhong, Z., Yan, J., Wu, W., Shao, J., & Liu, C.-L. (2018). Practical block-wise neural network architecture generation. In Real (10.1016/j.eswa.2021.115937_b48) 2017 Stanley (10.1016/j.eswa.2021.115937_b58) 2002; 10 Chollet (10.1016/j.eswa.2021.115937_b12) 2015 Klein (10.1016/j.eswa.2021.115937_b29) 2017; vol. 54 10.1016/j.eswa.2021.115937_b47 Liu (10.1016/j.eswa.2021.115937_b37) 2017 Suganuma (10.1016/j.eswa.2021.115937_b60) 2017 Mitchell (10.1016/j.eswa.2021.115937_b43) 1998 Klambauer (10.1016/j.eswa.2021.115937_b28) 2017 Zoph (10.1016/j.eswa.2021.115937_b70) 2017 Li (10.1016/j.eswa.2021.115937_b36) 2014 Zhang (10.1016/j.eswa.2021.115937_b67) 2017 Kingma (10.1016/j.eswa.2021.115937_b26) 2014 Cai (10.1016/j.eswa.2021.115937_b9) 2018 Javadi (10.1016/j.eswa.2021.115937_b23) 2019; 109 Kotikalapudi (10.1016/j.eswa.2021.115937_b30) 2017 Shaker (10.1016/j.eswa.2021.115937_b55) 2017 Baker (10.1016/j.eswa.2021.115937_b6) 2017 Alegre (10.1016/j.eswa.2021.115937_b4) 2008; 38 Maree (10.1016/j.eswa.2021.115937_b39) 2010; 25 Elsken (10.1016/j.eswa.2021.115937_b14) 2018 Krizhevsky (10.1016/j.eswa.2021.115937_b33) 2012 Pham (10.1016/j.eswa.2021.115937_b45) 2018 Bijar (10.1016/j.eswa.2021.115937_b7) 2012; 5 10.1016/j.eswa.2021.115937_b15 10.1016/j.eswa.2021.115937_b13 10.1016/j.eswa.2021.115937_b57 Riordon (10.1016/j.eswa.2021.115937_b49) 2019 Meyer-Lee (10.1016/j.eswa.2021.115937_b41) 2017 10.1016/j.eswa.2021.115937_b52 Krizhevsky (10.1016/j.eswa.2021.115937_b31) 2009 LeCun (10.1016/j.eswa.2021.115937_b35) 2012 Chang (10.1016/j.eswa.2021.115937_b11) 2014; 117 Ramos (10.1016/j.eswa.2021.115937_b46) 2004; 25 Voulodimos (10.1016/j.eswa.2021.115937_b63) 2018; 2018 Baker (10.1016/j.eswa.2021.115937_b5) 2017 Snoek (10.1016/j.eswa.2021.115937_b56) 2012 Shahriari (10.1016/j.eswa.2021.115937_b54) 2016; 104 Ilhan (10.1016/j.eswa.2021.115937_b21) 2018; 41 Selvaraju (10.1016/j.eswa.2021.115937_b53) 2016 Sánchez (10.1016/j.eswa.2021.115937_b50) 2005 Zoph (10.1016/j.eswa.2021.115937_b69) 2016 Zeiler (10.1016/j.eswa.2021.115937_b66) 2013 Esteva (10.1016/j.eswa.2021.115937_b16) 2019; 25 Kitano (10.1016/j.eswa.2021.115937_b27) 1990; 4 10.1016/j.eswa.2021.115937_b68 McCallum (10.1016/j.eswa.2021.115937_b40) 2019; 2 Miller (10.1016/j.eswa.2021.115937_b42) 1989; 89 Vicente-Fiel (10.1016/j.eswa.2021.115937_b62) 2013; 139 Boumaza (10.1016/j.eswa.2021.115937_b8) 2018; 2 Sanchez (10.1016/j.eswa.2021.115937_b51) 2006; 52 Abadi (10.1016/j.eswa.2021.115937_b1) 2016; 16 Kandasamy (10.1016/j.eswa.2021.115937_b25) 2018 Yániz (10.1016/j.eswa.2021.115937_b65) 2012; 77 Haugen (10.1016/j.eswa.2021.115937_b19) 2019 He (10.1016/j.eswa.2021.115937_b20) 2019 10.1016/j.eswa.2021.115937_b38 10.1016/j.eswa.2021.115937_b34 Abbiramy (10.1016/j.eswa.2021.115937_b2) 2010; 3 Jin (10.1016/j.eswa.2021.115937_b24) 2018 Otsu (10.1016/j.eswa.2021.115937_b44) 1979; 9 Xie (10.1016/j.eswa.2021.115937_b64) 2017 Gardner Jr (10.1016/j.eswa.2021.115937_b17) 1985; 4 Chang (10.1016/j.eswa.2021.115937_b10) 2017; 83 Isidori (10.1016/j.eswa.2021.115937_b22) 2005; 72 Abràmoff (10.1016/j.eswa.2021.115937_b3) 2004; 11 Ghasemian (10.1016/j.eswa.2021.115937_b18) 2015; 122 Szegedy (10.1016/j.eswa.2021.115937_b61) 2016 Krizhevsky (10.1016/j.eswa.2021.115937_b32) 2012 Stouffs (10.1016/j.eswa.2021.115937_b59) 2008; 90
References_xml	– year: 2015 ident: b12 article-title: Keras – volume: 109 start-page: 182 year: 2019 end-page: 194 ident: b23 article-title: A novel deep learning method for automatic assessment of human sperm images publication-title: Computers in Biology and Medicine – year: 2017 ident: b55 article-title: Human sperm head morphology dataset (HuSHeM) publication-title: Mendeley Data – year: 2016 ident: b61 article-title: Inception-v4, inception-ResNet and the impact of residual connections on learning publication-title: CoRR – reference: ; IEEE (pp. 1–37). – year: 2017 ident: b5 article-title: Designing neural network architectures using reinforcement learning publication-title: 5th international conference on learning representations, 2017, Toulon, France, April 24-26, 2017, conference track proceedings – volume: 72 start-page: 314 year: 2005 end-page: 318 ident: b22 article-title: Treatment of male infertility publication-title: Contraception – volume: 25 start-page: 406 year: 2004 end-page: 411 ident: b46 article-title: Evaluation of ICSI-selected epididymal sperm samples of obstructive azoospermic males by the CKIA system publication-title: Journal of Andrology – volume: 117 start-page: 225 year: 2014 end-page: 237 ident: b11 article-title: Gold-standard and improved framework for sperm head segmentation publication-title: Computer Methods and Programs in Biomedicine – volume: 11 start-page: 36 year: 2004 end-page: 42 ident: b3 article-title: Image processing with ImageJ publication-title: Biophotonics International – volume: 139 start-page: 182 year: 2013 end-page: 189 ident: b62 article-title: A comparative study of sperm morphometric subpopulations in cattle, goat, sheep and pigs using a computer-assisted fluorescence method (CASMA-F) publication-title: Animal Reproduction Science – reference: . URL – start-page: 1388 year: 2017 end-page: 1397 ident: b64 article-title: Genetic CNN publication-title: ICCV – year: 2016 ident: b53 article-title: Grad-CAM: Why did you say that? – reference: Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019). Regularized evolution for image classifier architecture search. In – start-page: 1097 year: 2012 end-page: 1105 ident: b32 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in neural information processing systems – year: 2018 ident: b25 article-title: Neural architecture search with bayesian optimisation and optimal transport – year: 2013 ident: b66 article-title: Visualizing and understanding convolutional networks publication-title: CoRR – volume: 122 start-page: 409 year: 2015 end-page: 420 ident: b18 article-title: An efficient method for automatic morphological abnormality detection from human sperm images publication-title: Computer Methods and Programs in Biomedicine – volume: 89 start-page: 379 year: 1989 end-page: 384 ident: b42 article-title: Designing neural networks using genetic algorithms publication-title: ICGA – volume: 2 start-page: 250 year: 2019 ident: b40 article-title: Deep learning-based selection of human sperm with high DNA integrity publication-title: Communications Biology – volume: 25 start-page: 1369 year: 2010 end-page: 1382 ident: b39 article-title: Morphometric dimensions of the human sperm head depend on the staining method used publication-title: Human Reproduction – volume: 10 start-page: 99 year: 2002 end-page: 127 ident: b58 article-title: Evolving neural networks through augmenting topologies publication-title: Evolutionary Computation – year: 2019 ident: b19 article-title: VISEM: A multimodal video dataset of human spermatozoa – volume: 77 start-page: 1343 year: 2012 end-page: 1350 ident: b65 article-title: Automatic evaluation of ram sperm morphometry publication-title: Theriogenology – volume: 4 start-page: 461 year: 1990 end-page: 476 ident: b27 article-title: Designing neural networks using genetic algorithms with graph generation system publication-title: Complex Systems – volume: 2018 year: 2018 ident: b63 article-title: Deep learning for computer vision: A brief review publication-title: Computational Intelligence and Neuroscience – reference: (pp. 248–255). – year: 2018 ident: b14 article-title: Neural architecture search: A survey – year: 2017 ident: b37 article-title: Hierarchical representations for efficient architecture search – start-page: 338 year: 2017 end-page: 341 ident: b67 article-title: Animal sperm morphology analysis system based on computer vision publication-title: 2017 eighth international conference on intelligent control and information processing – volume: 16 start-page: 265 year: 2016 end-page: 283 ident: b1 article-title: Tensorflow: a system for large-scale machine learning publication-title: OSDI – reference: Deng, J., Dong, W., Socher, R., & and, L. L. 2009. ImageNet: A large-scale hierarchical image database. In – start-page: 88 year: 2005 end-page: 95 ident: b50 article-title: Statistical approach to boar semen head classification based on intracellular intensity distribution publication-title: International conference on computer analysis of images and patterns – volume: 104 start-page: 148 year: 2016 end-page: 175 ident: b54 article-title: Taking the human out of the loop: A review of bayesian optimization publication-title: Proceedings of the IEEE – volume: 90 start-page: 1787 year: 2008 end-page: 1791 ident: b59 article-title: Is there a role for the nuclear export factor 2 gene in male infertility? publication-title: Fertility and Sterility – year: 2017 ident: b6 article-title: Accelerating neural architecture search using performance prediction – year: 1998 ident: b43 article-title: An introduction to genetic algorithms – volume: 3 start-page: 19 year: 2010 end-page: 23 ident: b2 article-title: Spermatozoa segmentation and morphological parameter analysis based detection of teratozoospermia publication-title: International Journal of Computer Applications – volume: 5 start-page: 384 year: 2012 ident: b7 article-title: Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear publication-title: Journal of Biomedical Science and Engineering – volume: 83 start-page: 143 year: 2017 end-page: 150 ident: b10 article-title: Gold-standard for computer-assisted morphological sperm analysis publication-title: Computers in Biology and Medicine – year: 2014 ident: b26 article-title: Adam: A method for stochastic optimization – start-page: 108 year: 2014 end-page: 113 ident: b36 article-title: Human sperm health diagnosis with principal component analysis and K-nearest neighbor algorithm publication-title: Medical biometrics, 2014 international conference on – reference: (pp. 4780–4789). – volume: vol. 54 start-page: 528 year: 2017 end-page: 536 ident: b29 article-title: Fast Bayesian optimization of machine learning hyperparameters on large datasets publication-title: Proceedings of the 20th international conference on artificial intelligence and statistics – year: 2017 ident: b48 article-title: Large-scale evolution of image classifiers – volume: 52 start-page: 38 year: 2006 end-page: 43 ident: b51 article-title: Statistical approach to boar semen evaluation using intracellular intensity distribution of head images publication-title: Cellular and Molecular Biology – year: 2016 ident: b69 article-title: Neural architecture search with reinforcement learning – reference: Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., & Sundaram, N., et al. (2015). Scalable bayesian optimization using deep neural networks. In – volume: 38 start-page: 461 year: 2008 end-page: 468 ident: b4 article-title: Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ publication-title: Computers in Biology and Medicine – year: 2019 ident: b49 article-title: Deep learning for the classification of human sperm publication-title: Computers in Biology and Medicine – volume: 2 start-page: 301 year: 2018 end-page: 307 ident: b8 article-title: Automatic human sperm concentrartion in microscopic videos publication-title: Medical Technologies Journal – start-page: 1097 year: 2012 end-page: 1105 ident: b33 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in neural information processing systems 25 – reference: Elsken, T., Metzen, J. H., & Hutter, F. 2019. Efficient multi-objective neural architecture search via Lamarckian evolution. In – reference: (pp. 2278–2324). – reference: Zhong, Z., Yan, J., Wu, W., Shao, J., & Liu, C.-L. (2018). Practical block-wise neural network architecture generation. In – year: 2017 ident: b70 article-title: Learning transferable architectures for scalable image recognition – reference: (pp. 2423–2432). – reference: (pp. 2171–2180). – year: 2009 ident: b31 article-title: Learning multiple layers of features from tiny images – volume: 41 start-page: 129 year: 2018 end-page: 139 ident: b21 article-title: A novel data acquisition and analyzing approach to spermiogram tests publication-title: Biomedical Signal Processing and Control – year: 2017 ident: b30 article-title: Keras-vis – volume: 25 start-page: 24 year: 2019 ident: b16 article-title: A guide to deep learning in healthcare publication-title: Nature Medicine – volume: 4 start-page: 1 year: 1985 end-page: 28 ident: b17 article-title: Exponential smoothing: The state of the art publication-title: Journal of Forecasting – year: 2019 ident: b20 article-title: AutoML: A survey of the state-of-the-art – start-page: 9 year: 2012 end-page: 48 ident: b35 article-title: Efficient BackProp publication-title: Neural networks: tricks of the trade – start-page: 2951 year: 2012 end-page: 2959 ident: b56 article-title: Practical bayesian optimization of machine learning algorithms publication-title: Advances in neural information processing systems – reference: . – year: 2017 ident: b60 article-title: A genetic programming approach to designing convolutional neural network architectures publication-title: CoRR – start-page: 971 year: 2017 end-page: 980 ident: b28 article-title: Self-normalizing neural networks publication-title: Advances in neural information processing systems – year: 2017 ident: b41 article-title: Evolving deep neural networks publication-title: CoRR – reference: Schaffer, J. D., Whitley, D., & Eshelman, L. J. (1992). Combinations of genetic algorithms and neural networks: A survey of the state of the art. In – volume: 9 start-page: 62 year: 1979 end-page: 66 ident: b44 article-title: A threshold selection method from gray-level histograms publication-title: IEEE Transactions on Systems, Man, and Cybernetics – start-page: 4095 year: 2018 end-page: 4104 ident: b45 article-title: Efficient neural architecture search via parameters sharing publication-title: Proceedings of the 35th international conference on machine learning – reference: Liu, H., Simonyan, K., & Yang, Y. (2091). DARTS: Differentiable architecture search. In – year: 2018 ident: b9 article-title: Efficient architecture search by network transformation – year: 2018 ident: b24 article-title: Efficient neural architecture search with network morphism – reference: Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998. Gradient-based learning applied to document recognition. In – year: 2017 ident: 10.1016/j.eswa.2021.115937_b6 – volume: 139 start-page: 182 issue: 1–4 year: 2013 ident: 10.1016/j.eswa.2021.115937_b62 article-title: A comparative study of sperm morphometric subpopulations in cattle, goat, sheep and pigs using a computer-assisted fluorescence method (CASMA-F) publication-title: Animal Reproduction Science doi: 10.1016/j.anireprosci.2013.04.002 – year: 2019 ident: 10.1016/j.eswa.2021.115937_b19 – start-page: 2951 year: 2012 ident: 10.1016/j.eswa.2021.115937_b56 article-title: Practical bayesian optimization of machine learning algorithms – volume: 104 start-page: 148 issue: 1 year: 2016 ident: 10.1016/j.eswa.2021.115937_b54 article-title: Taking the human out of the loop: A review of bayesian optimization publication-title: Proceedings of the IEEE doi: 10.1109/JPROC.2015.2494218 – ident: 10.1016/j.eswa.2021.115937_b38 – volume: 16 start-page: 265 year: 2016 ident: 10.1016/j.eswa.2021.115937_b1 article-title: Tensorflow: a system for large-scale machine learning – year: 2017 ident: 10.1016/j.eswa.2021.115937_b30 – ident: 10.1016/j.eswa.2021.115937_b57 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b60 article-title: A genetic programming approach to designing convolutional neural network architectures publication-title: CoRR – year: 2019 ident: 10.1016/j.eswa.2021.115937_b20 – volume: 109 start-page: 182 year: 2019 ident: 10.1016/j.eswa.2021.115937_b23 article-title: A novel deep learning method for automatic assessment of human sperm images publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2019.04.030 – ident: 10.1016/j.eswa.2021.115937_b34 doi: 10.1109/5.726791 – volume: 122 start-page: 409 issue: 3 year: 2015 ident: 10.1016/j.eswa.2021.115937_b18 article-title: An efficient method for automatic morphological abnormality detection from human sperm images publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2015.08.013 – year: 2016 ident: 10.1016/j.eswa.2021.115937_b69 – year: 2013 ident: 10.1016/j.eswa.2021.115937_b66 article-title: Visualizing and understanding convolutional networks publication-title: CoRR – volume: 41 start-page: 129 year: 2018 ident: 10.1016/j.eswa.2021.115937_b21 article-title: A novel data acquisition and analyzing approach to spermiogram tests publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2017.11.009 – volume: 2 start-page: 301 issue: 4 year: 2018 ident: 10.1016/j.eswa.2021.115937_b8 article-title: Automatic human sperm concentrartion in microscopic videos publication-title: Medical Technologies Journal doi: 10.26415/2572-004X-vol2iss4p301-307 – volume: 3 start-page: 19 issue: 7 year: 2010 ident: 10.1016/j.eswa.2021.115937_b2 article-title: Spermatozoa segmentation and morphological parameter analysis based detection of teratozoospermia publication-title: International Journal of Computer Applications doi: 10.5120/743-1050 – year: 1998 ident: 10.1016/j.eswa.2021.115937_b43 – volume: 2018 year: 2018 ident: 10.1016/j.eswa.2021.115937_b63 article-title: Deep learning for computer vision: A brief review publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2018/7068349 – volume: 2 start-page: 250 issue: 1 year: 2019 ident: 10.1016/j.eswa.2021.115937_b40 article-title: Deep learning-based selection of human sperm with high DNA integrity publication-title: Communications Biology doi: 10.1038/s42003-019-0491-6 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b55 article-title: Human sperm head morphology dataset (HuSHeM) publication-title: Mendeley Data – start-page: 1388 year: 2017 ident: 10.1016/j.eswa.2021.115937_b64 article-title: Genetic CNN – ident: 10.1016/j.eswa.2021.115937_b13 doi: 10.1109/CVPR.2009.5206848 – volume: 83 start-page: 143 year: 2017 ident: 10.1016/j.eswa.2021.115937_b10 article-title: Gold-standard for computer-assisted morphological sperm analysis publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2017.03.004 – volume: vol. 54 start-page: 528 year: 2017 ident: 10.1016/j.eswa.2021.115937_b29 article-title: Fast Bayesian optimization of machine learning hyperparameters on large datasets – volume: 77 start-page: 1343 issue: 7 year: 2012 ident: 10.1016/j.eswa.2021.115937_b65 article-title: Automatic evaluation of ram sperm morphometry publication-title: Theriogenology doi: 10.1016/j.theriogenology.2011.10.039 – year: 2018 ident: 10.1016/j.eswa.2021.115937_b25 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b37 – ident: 10.1016/j.eswa.2021.115937_b47 doi: 10.1609/aaai.v33i01.33014780 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b48 – start-page: 1097 year: 2012 ident: 10.1016/j.eswa.2021.115937_b32 article-title: Imagenet classification with deep convolutional neural networks – year: 2016 ident: 10.1016/j.eswa.2021.115937_b61 article-title: Inception-v4, inception-ResNet and the impact of residual connections on learning publication-title: CoRR – volume: 89 start-page: 379 year: 1989 ident: 10.1016/j.eswa.2021.115937_b42 article-title: Designing neural networks using genetic algorithms – volume: 25 start-page: 406 issue: 3 year: 2004 ident: 10.1016/j.eswa.2021.115937_b46 article-title: Evaluation of ICSI-selected epididymal sperm samples of obstructive azoospermic males by the CKIA system publication-title: Journal of Andrology doi: 10.1002/j.1939-4640.2004.tb02807.x – year: 2019 ident: 10.1016/j.eswa.2021.115937_b49 article-title: Deep learning for the classification of human sperm publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2019.103342 – start-page: 971 year: 2017 ident: 10.1016/j.eswa.2021.115937_b28 article-title: Self-normalizing neural networks – year: 2018 ident: 10.1016/j.eswa.2021.115937_b9 – volume: 25 start-page: 24 issue: 1 year: 2019 ident: 10.1016/j.eswa.2021.115937_b16 article-title: A guide to deep learning in healthcare publication-title: Nature Medicine doi: 10.1038/s41591-018-0316-z – year: 2017 ident: 10.1016/j.eswa.2021.115937_b70 – ident: 10.1016/j.eswa.2021.115937_b52 doi: 10.1109/COGANN.1992.273950 – year: 2016 ident: 10.1016/j.eswa.2021.115937_b53 – start-page: 4095 year: 2018 ident: 10.1016/j.eswa.2021.115937_b45 article-title: Efficient neural architecture search via parameters sharing – year: 2015 ident: 10.1016/j.eswa.2021.115937_b12 – volume: 38 start-page: 461 issue: 4 year: 2008 ident: 10.1016/j.eswa.2021.115937_b4 article-title: Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2008.01.005 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b5 article-title: Designing neural network architectures using reinforcement learning – start-page: 9 year: 2012 ident: 10.1016/j.eswa.2021.115937_b35 article-title: Efficient BackProp – volume: 72 start-page: 314 issue: 4 year: 2005 ident: 10.1016/j.eswa.2021.115937_b22 article-title: Treatment of male infertility publication-title: Contraception doi: 10.1016/j.contraception.2005.05.007 – start-page: 338 year: 2017 ident: 10.1016/j.eswa.2021.115937_b67 article-title: Animal sperm morphology analysis system based on computer vision – volume: 5 start-page: 384 issue: 07 year: 2012 ident: 10.1016/j.eswa.2021.115937_b7 article-title: Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear publication-title: Journal of Biomedical Science and Engineering doi: 10.4236/jbise.2012.57049 – year: 2017 ident: 10.1016/j.eswa.2021.115937_b41 article-title: Evolving deep neural networks publication-title: CoRR – year: 2009 ident: 10.1016/j.eswa.2021.115937_b31 – volume: 9 start-page: 62 issue: 1 year: 1979 ident: 10.1016/j.eswa.2021.115937_b44 article-title: A threshold selection method from gray-level histograms publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1979.4310076 – volume: 90 start-page: 1787 issue: 5 year: 2008 ident: 10.1016/j.eswa.2021.115937_b59 article-title: Is there a role for the nuclear export factor 2 gene in male infertility? publication-title: Fertility and Sterility doi: 10.1016/j.fertnstert.2007.08.071 – start-page: 108 year: 2014 ident: 10.1016/j.eswa.2021.115937_b36 article-title: Human sperm health diagnosis with principal component analysis and K-nearest neighbor algorithm – volume: 52 start-page: 38 issue: 6 year: 2006 ident: 10.1016/j.eswa.2021.115937_b51 article-title: Statistical approach to boar semen evaluation using intracellular intensity distribution of head images publication-title: Cellular and Molecular Biology – volume: 4 start-page: 461 issue: 4 year: 1990 ident: 10.1016/j.eswa.2021.115937_b27 article-title: Designing neural networks using genetic algorithms with graph generation system publication-title: Complex Systems – volume: 10 start-page: 99 issue: 2 year: 2002 ident: 10.1016/j.eswa.2021.115937_b58 article-title: Evolving neural networks through augmenting topologies publication-title: Evolutionary Computation doi: 10.1162/106365602320169811 – year: 2018 ident: 10.1016/j.eswa.2021.115937_b24 – year: 2018 ident: 10.1016/j.eswa.2021.115937_b14 – ident: 10.1016/j.eswa.2021.115937_b68 doi: 10.1109/CVPR.2018.00257 – volume: 11 start-page: 36 issue: 7 year: 2004 ident: 10.1016/j.eswa.2021.115937_b3 article-title: Image processing with ImageJ publication-title: Biophotonics International – volume: 4 start-page: 1 issue: 1 year: 1985 ident: 10.1016/j.eswa.2021.115937_b17 article-title: Exponential smoothing: The state of the art publication-title: Journal of Forecasting doi: 10.1002/for.3980040103 – year: 2014 ident: 10.1016/j.eswa.2021.115937_b26 – ident: 10.1016/j.eswa.2021.115937_b15 doi: 10.1007/978-3-030-05318-5_3 – start-page: 1097 year: 2012 ident: 10.1016/j.eswa.2021.115937_b33 article-title: ImageNet classification with deep convolutional neural networks – start-page: 88 year: 2005 ident: 10.1016/j.eswa.2021.115937_b50 article-title: Statistical approach to boar semen head classification based on intracellular intensity distribution – volume: 25 start-page: 1369 issue: 6 year: 2010 ident: 10.1016/j.eswa.2021.115937_b39 article-title: Morphometric dimensions of the human sperm head depend on the staining method used publication-title: Human Reproduction doi: 10.1093/humrep/deq075 – volume: 117 start-page: 225 issue: 2 year: 2014 ident: 10.1016/j.eswa.2021.115937_b11 article-title: Gold-standard and improved framework for sperm head segmentation publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2014.06.018
SSID	ssj0017007
Score	2.5062916
Snippet	Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem....
SourceID	hal proquest crossref elsevier
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	115937
SubjectTerms	Artificial Intelligence Artificial neural networks Bioinformatics Computer architecture Computer Science Datasets Deep learning Evaluation Fitness Genetic algorithm Genetic algorithms Human sperm morphometry Infertility Machine learning Medical imaging Morphology Neural architecture search Neural networks Search algorithms Search methods Sperm
Title	Genetic Neural Architecture Search for automatic assessment of human sperm images
URI	https://dx.doi.org/10.1016/j.eswa.2021.115937 https://www.proquest.com/docview/2608496653 https://hal.science/hal-03585035
Volume	188
WOSCitedRecordID	wos000768193500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELZg44EXfiMKA1mItylT4rqx_VihoYFgAm1IfbPixGaZ1nRqsrH997uLnaStYAIkXqLKaurq7vP5fL77jpB3KlbGWQ4LKU1cxA3LoizNRZQJEzvbXsXEbbMJcXgoZzP1NeSq1m07AVFV8upKnf9XVcMYKBtLZ_9C3f2PwgB8BqXDE9QOzz9SPBJJIwsr0m6g_FdvCnxysc-cvGgWga61J-dsPcc2qo_84fPdcg7Wpl4L3iMzchP4n7vKuJU78F5_ZdZ2CwY76wb8fSmXsCljrN7nBhzZa_B3p2Zx9uMEdtN5H5nO6mVXfVOuBSbgTBuvJXn0FTNDepIPO4qIJ74zz571RleKcZQK3ylxsMrylxbeBxtO92z9E2mjWAJGf6I8ccwGc_YRToZzMSQxU9jufJuJiQL7vT39uD_71F83idjX1Xd_LlRX-UTAzZl-58HcPcFU2o0dvXVTjh-RB-F8QaceF4_JHVs9IQ-73h00mPKn5FuACfUwoaswoR4mFGBCe5jQASZ04WgLE9rChHqYPCPfP-wfvz-IQnuNKOeCNZEqjJHWCeFEgauy4NJxhwRvRmWMF1amhQQHz0xSnjjwZApZgDfoitgJyy0bPydb1aKyLwiFbS-Ho0NsXA7SlsywXIArmWfgHkkm2Igknch0HrjnsQXKme6SDE81ilmjmLUX84js9u-ce-aVW7896TShg-_ofUINwLn1vbegtn4CJFs_mH7WOBaP4SgNj8tkRHY6reqw0mvN0lhylaaT8ct_nPsVuT-smx2y1Swv7GtyL79synr5JmD0BvLEqmo
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Genetic+Neural+Architecture+Search+for+automatic+assessment+of+human+sperm+images&rft.jtitle=Expert+systems+with+applications&rft.au=Miahi%2C+Erfan&rft.au=Mirroshandel%2C+Seyed+Abolghasem&rft.au=Nasr%2C+Alexis&rft.date=2022-02-01&rft.pub=Elsevier+Ltd&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=188&rft_id=info:doi/10.1016%2Fj.eswa.2021.115937&rft.externalDocID=S0957417421012914
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon