Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis
Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis...
Uloženo v:
| Vydáno v: | International journal of imaging systems and technology Ročník 35; číslo 6 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Wiley Subscription Services, Inc
01.11.2025
|
| Témata: | |
| ISSN: | 0899-9457, 1098-1098 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis. It integrates an improved optimization method with a hybridized CNN architecture. In this article, a custom CNN, feed‐forward and backpropagation have been implemented. The scaled conjugate algorithm is employed in the feed‐forward paradigm, yielding a formidable accuracy of 99.1%. On the other hand, backpropagation implements stochastic gradient descent and exhibits a remarkable accuracy rate of 97.3%. Additionally, by integrating the grey wolf optimization (GWO) algorithm with the Backpropagation Neural Network (BPNN), model performance is enhanced by optimizing parameters and accuracy to 100%. Furthermore, the custom CNN achieves an incredible 98% accuracy by utilizing the Adam optimizer in conjunction with the ReduceLROnPlateau approach. Statistical analysis utilizing Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) tests has demonstrated that the suggested hybrid model improves detection accuracy and reliability. These results highlight the adaptability and effectiveness of various optimization techniques in enhancing the performance of neural network models on a range of demanding tasks related to machine learning and pattern recognition. |
|---|---|
| AbstractList | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis. It integrates an improved optimization method with a hybridized CNN architecture. In this article, a custom CNN, feed‐forward and backpropagation have been implemented. The scaled conjugate algorithm is employed in the feed‐forward paradigm, yielding a formidable accuracy of 99.1%. On the other hand, backpropagation implements stochastic gradient descent and exhibits a remarkable accuracy rate of 97.3%. Additionally, by integrating the grey wolf optimization (GWO) algorithm with the Backpropagation Neural Network (BPNN), model performance is enhanced by optimizing parameters and accuracy to 100%. Furthermore, the custom CNN achieves an incredible 98% accuracy by utilizing the Adam optimizer in conjunction with the ReduceLROnPlateau approach. Statistical analysis utilizing Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) tests has demonstrated that the suggested hybrid model improves detection accuracy and reliability. These results highlight the adaptability and effectiveness of various optimization techniques in enhancing the performance of neural network models on a range of demanding tasks related to machine learning and pattern recognition. |
| Author | Usman, Muhammad Pu, Juhua Batool, Shumaila Zainab, Saima |
| Author_xml | – sequence: 1 givenname: Shumaila surname: Batool fullname: Batool, Shumaila organization: Department of Mathematics The Women University Multan Multan Pakistan – sequence: 2 givenname: Saima surname: Zainab fullname: Zainab, Saima organization: Department of Mathematics The Women University Multan Multan Pakistan – sequence: 3 givenname: Muhammad orcidid: 0000-0001-7776-6811 surname: Usman fullname: Usman, Muhammad organization: School of Computer Science and Engineering Beihang University Beijing China – sequence: 4 givenname: Juhua surname: Pu fullname: Pu, Juhua organization: School of Computer Science and Engineering Beihang University Beijing China |
| BookMark | eNotkEFPwjAYhhuDiYAe_AdNPHkYtl27rkedICYELnjy0HSl1SJrsR0h-OvdxMv75U2efG_yjMDAB28AuMVoghEiD65RE44IIxdgiJEosz4GYIhKITJBGb8Co5S2CGHMEBuC99W-dY37MRs4P9XRbWC1XMJZVI05hvgFbYhw6j-V1x2xPjRdrXYqJWedVq0LHjoPn6JRqYVVT0X47NSHD8mla3Bp1S6Zm_87Bm-z6bqaZ4vVy2v1uMg0YbTNSsGULoyyvFAs58RwyhWuCeXMFBprXjBT2lojLHKumNHWCop5WddcWWpIPgZ357_7GL4PJrVyGw7Rd5MyJ5xRRjnpqfszpWNIKRor97GzFU8SI9m7k12Tf-7yXzqZY18 |
| Cites_doi | 10.1007/s10853‐024‐09799‐8 10.1016/j.iswa.2022.20 10.3390/cancers15030885 10.1016/j.asej.2024.102734 10.32604/cmc.2023.031723 10.3390/diagnostics13132191 10.1109/ACCESS.2020.3021557 10.1109/ACCESS.2023.3310429 10.1016/j.mlwa.2024.100555 10.1007/s11042‐023‐16431‐5 10.1007/s00521‐022‐07895‐x 10.1016/j.advengsoft.2013.12.007 10.3390/jimaging5030037 10.1016/j.compbiomed.2022.105812 10.1038/s41598‐020‐67441‐4 10.1101/2022.04.29.22274513 10.3390/diagnostics13091582 10.1007/s00521‐023‐08771‐y 10.3390/app12041957 10.61356/j.mawa.2024.3237 10.3390/app13010156 10.1109/ACCESS.2022.3212081 10.1109/ITSIM.2010.5561599 10.54021/seesv5n1‐008 10.1038/s41598-024-67424-9 10.17559/TV‐20230621000751 10.1002/ima.23133 10.1109/ACCESS.2023.3298955 10.31557/APJCP.2023.24.2.531 10.1007/s10916‐011‐9768‐0 10.3390/healthcare8020111 10.1007/s00521‐022‐07445‐5 10.1007/s12652‐022‐03713‐3 10.1007/s00530‐024‐01295‐y 10.1186/s12880‐023‐00964‐0 10.3390/diagnostics13172746 10.1016/j.bbe.2023.12.003 10.1016/j.physa.2019.123592 10.1007/s42979‐020‐00305‐w 10.1007/s00521‐023‐09202‐8 10.36227/techrxiv.23283533 |
| ContentType | Journal Article |
| Copyright | 2025 Wiley Periodicals, LLC. |
| Copyright_xml | – notice: 2025 Wiley Periodicals, LLC. |
| DBID | AAYXX CITATION |
| DOI | 10.1002/ima.70252 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| EISSN | 1098-1098 |
| ExternalDocumentID | 10_1002_ima_70252 |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHQN AAIPD AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAYXX AAZKR ABCQN ABCUV ABDBF ABEML ABIJN ABJNI ABQWH ABXGK ACAHQ ACBWZ ACCZN ACGFS ACGOF ACMXC ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AIACR AIDQK AIDYY AIQQE AITYG AIURR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 CITATION CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBS EJD ESX F00 F01 F04 F5P FEDTE FUBAC G-S G.N GNP GODZA H.X HDBZQ HF~ HGLYW HHY HVGLF HZ~ I-F IX1 J0M JPC KBYEO KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M65 MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O8X O9- OIG P2P P2W P2X P2Z P4B P4D PALCI Q.N Q11 QB0 QRW R.K RIWAO RJQFR ROL RX1 RYL SAMSI SUPJJ TUS UB1 V2E W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WOHZO WQJ WVDHM WXI WXSBR XG1 XPP XV2 ZZTAW ~02 ~IA ~WT |
| ID | FETCH-LOGICAL-c254t-895ac6eaf76a5372e747a1b2475e6c1c765e8fbc01937a5ecff94178bb7af4e23 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001611489000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0899-9457 |
| IngestDate | Fri Nov 28 08:12:31 EST 2025 Sat Nov 29 06:53:14 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c254t-895ac6eaf76a5372e747a1b2475e6c1c765e8fbc01937a5ecff94178bb7af4e23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7776-6811 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.70252 |
| PQID | 3275454722 |
| PQPubID | 1026352 |
| ParticipantIDs | proquest_journals_3275454722 crossref_primary_10_1002_ima_70252 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-11-01 |
| PublicationDateYYYYMMDD | 2025-11-01 |
| PublicationDate_xml | – month: 11 year: 2025 text: 2025-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | International journal of imaging systems and technology |
| PublicationYear | 2025 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | e_1_2_16_25_1 e_1_2_16_23_1 Yu M. (e_1_2_16_29_1) 2017 e_1_2_16_27_1 Salaheldin A. M. (e_1_2_16_44_1) 2024; 34 e_1_2_16_42_1 e_1_2_16_21_1 e_1_2_16_40_1 Lu S.‐Y. (e_1_2_16_18_1) 2022; 148 Atteia G. (e_1_2_16_46_1) 2023; 75 e_1_2_16_15_1 e_1_2_16_38_1 Azmi M. S. B. M. (e_1_2_16_35_1) 2010 Mirjalili S. (e_1_2_16_37_1) 2014; 69 e_1_2_16_13_1 e_1_2_16_19_1 Shah A. (e_1_2_16_50_1) 2025 e_1_2_16_17_1 e_1_2_16_36_1 e_1_2_16_30_1 e_1_2_16_11_1 Singhal S. (e_1_2_16_32_1) 2018 e_1_2_16_7_1 e_1_2_16_9_1 e_1_2_16_3_1 e_1_2_16_5_1 e_1_2_16_26_1 e_1_2_16_24_1 Saleh N. (e_1_2_16_43_1) 2024; 14 Khodadadi N. (e_1_2_16_51_1) 2023; 11 e_1_2_16_45_1 e_1_2_16_28_1 e_1_2_16_41_1 e_1_2_16_22_1 e_1_2_16_20_1 Khodadadi N. (e_1_2_16_52_1) 2022; 10 e_1_2_16_14_1 e_1_2_16_39_1 El‐Kenawy E. S. M. (e_1_2_16_48_1) 2025; 9 e_1_2_16_12_1 e_1_2_16_16_1 e_1_2_16_31_1 e_1_2_16_33_1 e_1_2_16_10_1 Awujoola O. J. (e_1_2_16_2_1) 2024 Abdalla O. A. (e_1_2_16_34_1) 2010 Abdelhamid A. A. (e_1_2_16_47_1) 2023; 11 e_1_2_16_8_1 e_1_2_16_4_1 e_1_2_16_6_1 Dutta P. K. (e_1_2_16_49_1) 2025; 4 |
| References_xml | – ident: e_1_2_16_39_1 doi: 10.1007/s10853‐024‐09799‐8 – ident: e_1_2_16_12_1 doi: 10.1016/j.iswa.2022.20 – ident: e_1_2_16_13_1 doi: 10.3390/cancers15030885 – ident: e_1_2_16_25_1 doi: 10.1016/j.asej.2024.102734 – volume: 75 start-page: 1883 issue: 1 year: 2023 ident: e_1_2_16_46_1 article-title: Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data publication-title: Computers, Materials & Continua doi: 10.32604/cmc.2023.031723 – ident: e_1_2_16_7_1 doi: 10.3390/diagnostics13132191 – ident: e_1_2_16_9_1 doi: 10.1109/ACCESS.2020.3021557 – volume: 11 start-page: 94094 year: 2023 ident: e_1_2_16_51_1 article-title: BAOA: Binary Arithmetic Optimization Algorithm With K‐Nearest Neighbor Classifier for Feature Selection publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3310429 – ident: e_1_2_16_26_1 doi: 10.1016/j.mlwa.2024.100555 – ident: e_1_2_16_24_1 doi: 10.1007/s11042‐023‐16431‐5 – ident: e_1_2_16_30_1 – volume: 4 start-page: 33 year: 2025 ident: e_1_2_16_49_1 article-title: Ethical Challenges and Regulatory Compliance in AI‐Driven Neurological Diagnostics: A Review of Standards and Practices publication-title: Metaheuristic Optimization Review – start-page: 221 volume-title: Machine Learning Algorithms Using Scikit and TensorFlow Environments year: 2024 ident: e_1_2_16_2_1 – ident: e_1_2_16_16_1 doi: 10.1007/s00521‐022‐07895‐x – volume: 69 start-page: 46 year: 2014 ident: e_1_2_16_37_1 article-title: Grey Wolf Optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – ident: e_1_2_16_23_1 doi: 10.3390/jimaging5030037 – volume: 148 year: 2022 ident: e_1_2_16_18_1 article-title: SAFNet: A Deep Spatial Attention Network With Classifier Fusion for Breast Cancer Detection publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105812 – ident: e_1_2_16_20_1 doi: 10.1038/s41598‐020‐67441‐4 – ident: e_1_2_16_31_1 doi: 10.1101/2022.04.29.22274513 – ident: e_1_2_16_19_1 doi: 10.3390/diagnostics13091582 – ident: e_1_2_16_27_1 – volume-title: Breast Cancer Prediction Using Machine Learning Algorithm. Approved by Supervising Committee: M. J. Daniels and E. C. Theriot year: 2017 ident: e_1_2_16_29_1 – ident: e_1_2_16_17_1 doi: 10.1007/s00521‐023‐08771‐y – start-page: 164 volume-title: 2010 IEEE Student Conference on Research and Development (SCOReD) year: 2010 ident: e_1_2_16_35_1 – ident: e_1_2_16_5_1 doi: 10.3390/app12041957 – ident: e_1_2_16_14_1 doi: 10.61356/j.mawa.2024.3237 – ident: e_1_2_16_6_1 doi: 10.3390/app13010156 – volume: 10 start-page: 106673 year: 2022 ident: e_1_2_16_52_1 article-title: An Archive‐Based Multi‐Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3212081 – start-page: 994 volume-title: 2010 International Symposium on Information Technology year: 2010 ident: e_1_2_16_34_1 doi: 10.1109/ITSIM.2010.5561599 – ident: e_1_2_16_40_1 doi: 10.54021/seesv5n1‐008 – volume: 14 issue: 1 year: 2024 ident: e_1_2_16_43_1 article-title: Skin Cancer Classification Based on an Optimized Convolutional Neural Network and Multicriteria Decision‐Making publication-title: Scientific Reports doi: 10.1038/s41598-024-67424-9 – ident: e_1_2_16_36_1 doi: 10.17559/TV‐20230621000751 – volume: 34 issue: 4 year: 2024 ident: e_1_2_16_44_1 article-title: Deep Learning‐Based Automated Detection and Grading of Papilledema From OCT Images: A Promising Approach for Improved Clinical Diagnosis and Management publication-title: International Journal of Imaging Systems and Technology doi: 10.1002/ima.23133 – volume: 11 start-page: 79750 year: 2023 ident: e_1_2_16_47_1 article-title: Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3298955 – ident: e_1_2_16_4_1 doi: 10.31557/APJCP.2023.24.2.531 – volume: 9 start-page: 1 year: 2025 ident: e_1_2_16_48_1 article-title: Hybrid Ensemble Learning for Flow‐Level IoT Traffic Classification Using ACI Dataset: Towards Scalable and Real‐Time Threat Detection publication-title: Journal of Artificial Intelligence and Metaheuristics – ident: e_1_2_16_33_1 doi: 10.1007/s10916‐011‐9768‐0 – ident: e_1_2_16_45_1 doi: 10.3390/healthcare8020111 – ident: e_1_2_16_15_1 doi: 10.1007/s00521‐022‐07445‐5 – ident: e_1_2_16_22_1 doi: 10.1007/s12652‐022‐03713‐3 – ident: e_1_2_16_8_1 doi: 10.1007/s00530‐024‐01295‐y – ident: e_1_2_16_21_1 doi: 10.1186/s12880‐023‐00964‐0 – start-page: 464 volume-title: 2018 2nd International Conference on I‐SMAC (IoT in Social, Mobile, Analytics and Cloud) year: 2018 ident: e_1_2_16_32_1 – ident: e_1_2_16_28_1 – ident: e_1_2_16_3_1 doi: 10.3390/diagnostics13172746 – ident: e_1_2_16_11_1 doi: 10.1016/j.bbe.2023.12.003 – ident: e_1_2_16_42_1 doi: 10.1016/j.physa.2019.123592 – volume-title: Breast Ultrasound Images Dataset year: 2025 ident: e_1_2_16_50_1 – ident: e_1_2_16_10_1 doi: 10.1007/s42979‐020‐00305‐w – ident: e_1_2_16_38_1 doi: 10.1007/s00521‐023‐09202‐8 – ident: e_1_2_16_41_1 doi: 10.36227/techrxiv.23283533 |
| SSID | ssj0011505 |
| Score | 2.3920932 |
| Snippet | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Index Database |
| SubjectTerms | Abnormalities Accuracy Algorithms Artificial neural networks Back propagation Back propagation networks Breast cancer Classification Diagnosis Effectiveness Machine learning Medical diagnosis Medical imaging Neural networks Optimization Optimization techniques Pattern recognition Performance enhancement Statistical analysis Tumors Variance analysis |
| Title | Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis |
| URI | https://www.proquest.com/docview/3275454722 |
| Volume | 35 |
| WOSCitedRecordID | wos001611489000001&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: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1098-1098 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011505 issn: 0899-9457 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELZKBxJ7QDCY2BjIQoiXKaN14jh5ZKXTHkaGoJUqeIhsx1EjLaE0yTT4u_gDOdtJmgqExgMvVuJE-eH7dD6f775D6FUIqPAS4TohHUnH45Q7gvKRIymVDPCcKJGaYhMsioLFIvwwGPxsc2Gur1hRBDc34eq_ihr6QNg6dfYfxN09FDrgGIQOLYgd2lsJ_hKUQJ79AEPy_LtOxzqeRJG2T20MlgkrnBZLu_E_q3M4NYUxdcgQbyMfT3WoenU80XetQS2acLys7Fuy267EPgFFbgsfWY5oSwFd_ebAP4XFvi3u9UmXCcyuuunhs8nnst7qbDNrzMvGV_u-XvI850mn1GubXbKsed-FQWiTy3d7RdlXippR1LOk1ifKKm3Niaqbvla3JCgNev0_ThaWfBb-5ITBR5HNjNhGAUSX8dn84iKeTRez16tvjq5Vpvf0m8Itd9AOYTQMhmjn3Ue4sdu9AiPbhM6239oyWo3Im-5t23bQthlgbJvZQ_SgWZTgtxZMj9BAFXtot0dVuYfumVBhWT5GXzqAYQswDADDHcAwAAy3AMMGYHgbYDgrsAUYtgDDHcCeoPnZdDY5d5oSHY4k1KucIKRc-oqnzOfUZUTB6pSPBfEYVb4cS-ZTFaRCwkLCZZwqmaahN2aBEIynniLuPhoWXwv1FGEWJL4a-UEilPI8QYUYS11BlkgpkyQUB-hlO2DxyjKxxJZzm8RwFptRPUBH7VDGDfLL2AUpad46Qg7_fvkZur-B5xEaVutaPUd35XWVlesXjZR_ARGNji8 |
| linkProvider | Wiley-Blackwell |
| 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=Optimized+Hybrid+CNN+Framework+for+Enhanced+Tumor+Classification+in+Breast+Cancer+Diagnosis&rft.jtitle=International+journal+of+imaging+systems+and+technology&rft.au=Batool%2C+Shumaila&rft.au=Zainab%2C+Saima&rft.au=Usman%2C+Muhammad&rft.au=Pu%2C+Juhua&rft.date=2025-11-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=0899-9457&rft.eissn=1098-1098&rft.volume=35&rft.issue=6&rft_id=info:doi/10.1002%2Fima.70252&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0899-9457&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0899-9457&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0899-9457&client=summon |