Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images
We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian...
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| Veröffentlicht in: | Bioengineering (Basel) Jg. 9; H. 3; S. 97 |
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| Abstract | We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database. |
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| AbstractList | We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database. We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database.We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database. |
| Author | Delhommelle, Jerome Bechelli, Solene |
| AuthorAffiliation | 2 MetaSimulation of Nonequilibrium Processes (MSNEP) Group, Tech Accelerator, University of North Dakota, Grand Forks, ND 58202, USA 4 School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA 3 Department of Chemistry, University of North Dakota, Grand Forks, ND 58202, USA 1 Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND 58202, USA; solene.bechelli@und.edu |
| AuthorAffiliation_xml | – name: 2 MetaSimulation of Nonequilibrium Processes (MSNEP) Group, Tech Accelerator, University of North Dakota, Grand Forks, ND 58202, USA – name: 1 Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND 58202, USA; solene.bechelli@und.edu – name: 3 Department of Chemistry, University of North Dakota, Grand Forks, ND 58202, USA – name: 4 School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA |
| Author_xml | – sequence: 1 givenname: Solene surname: Bechelli fullname: Bechelli, Solene – sequence: 2 givenname: Jerome orcidid: 0000-0002-6498-6927 surname: Delhommelle fullname: Delhommelle, Jerome |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35324786$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jiph.2020.06.033 10.1109/SoCPaR.2009.80 10.1016/j.jid.2018.01.028 10.1016/j.bspc.2021.102631 10.1016/j.ejca.2021.06.049 10.1016/j.compbiolchem.2021.107619 10.1001/jamadermatol.2019.3807 10.1038/nature21056 10.3390/biom10081123 10.1016/j.compbiomed.2020.104065 10.1007/s11042-020-09388-2 10.7150/jca.28769 10.1049/iet-cvi.2018.5195 10.1001/archderm.143.1.101 10.1109/ICRTIT.2011.5972349 10.1016/j.bspc.2019.101678 10.1016/j.patrec.2020.05.019 10.1016/j.patrec.2019.12.006 10.1007/s10916-019-1413-3 10.1016/j.bspc.2021.102428 10.1109/ICET48972.2019.8994508 10.14740/wjon1349 10.1016/j.artmed.2015.04.004 10.1109/CVPR.2016.308 10.1147/JRD.2017.2708299 10.1109/AISP.2017.8324083 10.1007/s11704-019-8208-z 10.1109/MAPR49794.2020.9237778 10.1145/1143844.1143934 10.1016/j.ejca.2019.04.001 10.1016/S1470-2045(02)00679-4 10.1109/CVPR.2017.195 10.1016/j.patrec.2004.09.007 10.1007/978-3-030-40850-3_8 10.3390/diagnostics12010040 10.1021/acs.jpcc.9b07936 10.1097/00008390-199806000-00009 10.1109/ACCESS.2020.3002902 10.1109/CIBEC.2018.8641762 10.1016/j.bspc.2019.101581 10.1001/jamadermatol.2015.1187 10.1016/j.bspc.2019.101765 10.1016/S1532-0464(03)00034-0 10.1109/CVPR.2016.90 10.1001/archdermatol.2010.4 10.1109/AEECT.2017.8257738 10.1109/HNICEM.2018.8666296 10.1109/ICTAI.2011.29 10.1016/j.media.2020.101915 10.1002/widm.1249 10.1063/1.4801262 10.1038/nature14539 10.1038/sdata.2018.161 |
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| Keywords | deep learning skin cancer image classification machine learning convolutional neural network |
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| References | Kittler (ref_9) 2002; 3 ref_14 ref_58 Bi (ref_23) 2021; 68 ref_56 ref_11 ref_55 Ghassemi (ref_19) 2020; 57 Krogh (ref_40) 1995; 7 Bhuiyan (ref_54) 2013; 4 ref_17 ref_16 Nematzadeh (ref_28) 2022; 97 ref_15 Thomas (ref_30) 2021; 68 Fan (ref_10) 2020; 8 LeCun (ref_24) 2015; 521 Han (ref_52) 2018; 138 Koh (ref_3) 2007; 143 ref_29 Dong (ref_43) 2020; 14 ref_26 Wang (ref_18) 2021; 66 Tschandl (ref_34) 2018; 5 Li (ref_37) 1984; 40 Sagi (ref_41) 2018; 8 Liong (ref_57) 2013; 1522 Desgranges (ref_42) 2020; 124 ref_32 ref_31 Dreiseitl (ref_35) 2002; 35 Sharif (ref_13) 2020; 131 ref_39 ref_38 Rodrigues (ref_50) 2020; 136 Binder (ref_8) 1998; 8 Arevalo (ref_22) 2015; 64 Esteva (ref_7) 2017; 542 Guan (ref_51) 2019; 10 Han (ref_33) 2020; 156 Rundo (ref_25) 2018; 12 Saba (ref_20) 2019; 43 Chatterjee (ref_21) 2019; 53 Li (ref_36) 2005; 26 ref_47 Ali (ref_49) 2021; 5 ref_46 Rogers (ref_1) 2015; 151 ref_45 Stern (ref_2) 2010; 146 ref_44 Saba (ref_12) 2020; 13 Brinker (ref_59) 2019; 113 Codella (ref_4) 2017; 61 Naik (ref_5) 2021; 12 Pereira (ref_6) 2020; 57 ref_48 Maron (ref_27) 2021; 156 Chaturvedi (ref_53) 2020; 79 Goyal (ref_60) 2020; 127 |
| References_xml | – ident: ref_32 – ident: ref_55 – volume: 13 start-page: 1274 year: 2020 ident: ref_12 article-title: Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges publication-title: J. Infect. Public Health doi: 10.1016/j.jiph.2020.06.033 – ident: ref_15 doi: 10.1109/SoCPaR.2009.80 – volume: 138 start-page: 1529 year: 2018 ident: ref_52 article-title: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm publication-title: J. Investig. Dermatol. doi: 10.1016/j.jid.2018.01.028 – volume: 68 start-page: 102631 year: 2021 ident: ref_23 article-title: Computer-aided skin cancer diagnosis based on a new meta-heuristic algorithm combined with support vector method publication-title: Biomed. Signal Processing doi: 10.1016/j.bspc.2021.102631 – volume: 4 start-page: 1 year: 2013 ident: ref_54 article-title: Image processing for skin cancer features extraction publication-title: Int. J. Sci. Eng. Res. – volume: 156 start-page: 202 year: 2021 ident: ref_27 article-title: Skin cancer classification via convolutional neural networks: Systematic review of studies involving human experts publication-title: Eur. J. Cancer doi: 10.1016/j.ejca.2021.06.049 – volume: 97 start-page: 107619 year: 2022 ident: ref_28 article-title: Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2021.107619 – volume: 156 start-page: 29 year: 2020 ident: ref_33 article-title: Keratinocytic skin cancer detection on the face using region-based convolutional neural network publication-title: JAMA Dermatol. doi: 10.1001/jamadermatol.2019.3807 – volume: 542 start-page: 115 year: 2017 ident: ref_7 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature doi: 10.1038/nature21056 – ident: ref_31 doi: 10.3390/biom10081123 – volume: 127 start-page: 104065 year: 2020 ident: ref_60 article-title: Artificial intelligence-based image classification for diagnosis of skin cancer: Challenges and opportunities publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.104065 – volume: 79 start-page: 28477 year: 2020 ident: ref_53 article-title: A multi-class skin cancer classification using deep convolutional neural networks, Multimed publication-title: Tools Appl. doi: 10.1007/s11042-020-09388-2 – volume: 10 start-page: 4876 year: 2019 ident: ref_51 article-title: Deep convolutional neural network vgg-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: A pilot study publication-title: J. Cancer doi: 10.7150/jca.28769 – volume: 12 start-page: 957 year: 2018 ident: ref_25 article-title: Evaluation of levenberg–marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up publication-title: IET Comput. Vision doi: 10.1049/iet-cvi.2018.5195 – volume: 143 start-page: 101 year: 2007 ident: ref_3 article-title: Melanoma screening: Focusing the public health journey publication-title: Arch. Dermatol. doi: 10.1001/archderm.143.1.101 – ident: ref_38 doi: 10.1109/ICRTIT.2011.5972349 – volume: 57 start-page: 101678 year: 2020 ident: ref_19 article-title: Deep neural network with generative adversarial networks pre-training for brain tumor classification based on mr images publication-title: Biomed. Signal Processing doi: 10.1016/j.bspc.2019.101678 – volume: 136 start-page: 8 year: 2020 ident: ref_50 article-title: A new approach for classification skin lesion based on transfer learning, deep learning, and iot system publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2020.05.019 – volume: 131 start-page: 30 year: 2020 ident: ref_13 article-title: A comprehensive review on multi-organs tumor detection based on machine learning publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2019.12.006 – volume: 43 start-page: 289 year: 2019 ident: ref_20 article-title: Region extraction and classification of skin cancer: A heterogeneous framework of deep cnn features fusion and reduction publication-title: J. Med. Syst. doi: 10.1007/s10916-019-1413-3 – volume: 7 start-page: 231 year: 1995 ident: ref_40 article-title: Neural network ensembles, cross validation, and active learning publication-title: Adv. Neural Inf. Processing Syst. – volume: 66 start-page: 102428 year: 2021 ident: ref_18 article-title: Unlabeled skin lesion classification by self-supervised topology clustering network publication-title: Biomed. Signal Processing doi: 10.1016/j.bspc.2021.102428 – ident: ref_26 doi: 10.1109/ICET48972.2019.8994508 – volume: 12 start-page: 7 year: 2021 ident: ref_5 article-title: Cutaneous malignant melanoma: A review of early diagnosis and management publication-title: World J. Oncol. doi: 10.14740/wjon1349 – volume: 64 start-page: 131 year: 2015 ident: ref_22 article-title: An unsupervised feature learning framework for basal cell carcinoma image analysis publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2015.04.004 – ident: ref_48 doi: 10.1109/CVPR.2016.308 – volume: 61 start-page: 5:1 year: 2017 ident: ref_4 article-title: Deep learning ensembles for melanoma recognition in dermoscopy images publication-title: IBM J. Res. Dev. doi: 10.1147/JRD.2017.2708299 – ident: ref_39 doi: 10.1109/AISP.2017.8324083 – volume: 14 start-page: 241 year: 2020 ident: ref_43 article-title: A survey on ensemble learning publication-title: Front. Comput. Sci. doi: 10.1007/s11704-019-8208-z – volume: 5 start-page: 100036 year: 2021 ident: ref_49 article-title: An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models publication-title: Mach. Learn. Appl. – ident: ref_58 doi: 10.1109/MAPR49794.2020.9237778 – ident: ref_44 doi: 10.1145/1143844.1143934 – volume: 113 start-page: 47 year: 2019 ident: ref_59 article-title: Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task publication-title: Eur. J. Cancer doi: 10.1016/j.ejca.2019.04.001 – volume: 3 start-page: 159 year: 2002 ident: ref_9 article-title: Diagnostic accuracy of dermoscopy publication-title: Lancet Oncol. doi: 10.1016/S1470-2045(02)00679-4 – ident: ref_45 doi: 10.1109/CVPR.2017.195 – volume: 26 start-page: 527 year: 2005 ident: ref_36 article-title: 2d-lda: A statistical linear discriminant analysis for image matrix publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2004.09.007 – ident: ref_16 doi: 10.1007/978-3-030-40850-3_8 – ident: ref_29 doi: 10.3390/diagnostics12010040 – volume: 124 start-page: 1907 year: 2020 ident: ref_42 article-title: Ensemble learning of partition functions for the prediction of thermodynamic properties of adsorption in metal–organic and covalent organic frameworks publication-title: J. Phys. Chem. C doi: 10.1021/acs.jpcc.9b07936 – volume: 8 start-page: 261 year: 1998 ident: ref_8 article-title: Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network publication-title: Melanoma Res. doi: 10.1097/00008390-199806000-00009 – volume: 8 start-page: 131975 year: 2020 ident: ref_10 article-title: High voltage gain dc/dc converter using coupled inductor and vm techniques publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3002902 – ident: ref_17 doi: 10.1109/CIBEC.2018.8641762 – volume: 53 start-page: 101581 year: 2019 ident: ref_21 article-title: Extraction of features from cross correlation in space and frequency domains for classification of skin lesions publication-title: Biomed. Signal Processing doi: 10.1016/j.bspc.2019.101581 – volume: 40 start-page: 358 year: 1984 ident: ref_37 article-title: Classification and regression trees (cart) publication-title: Biometrics – volume: 151 start-page: 1081 year: 2015 ident: ref_1 article-title: Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population, 2012 publication-title: JAMA Dermatol. doi: 10.1001/jamadermatol.2015.1187 – ident: ref_46 – volume: 57 start-page: 101765 year: 2020 ident: ref_6 article-title: Skin lesion classification enhancement using border-line features—The melanoma vs nevus problem publication-title: Biomed. Signal Processing doi: 10.1016/j.bspc.2019.101765 – volume: 35 start-page: 352 year: 2002 ident: ref_35 article-title: Logistic regression and artificial neural network classification models: A methodology review publication-title: J. Biomed. Inform. doi: 10.1016/S1532-0464(03)00034-0 – ident: ref_47 doi: 10.1109/CVPR.2016.90 – volume: 146 start-page: 279 year: 2010 ident: ref_2 article-title: Prevalence of a history of skin cancer in 2007: Results of an incidence-based model publication-title: Arch. Dermatol. doi: 10.1001/archdermatol.2010.4 – ident: ref_14 doi: 10.1109/AEECT.2017.8257738 – ident: ref_11 doi: 10.1109/HNICEM.2018.8666296 – ident: ref_56 doi: 10.1109/ICTAI.2011.29 – volume: 68 start-page: 101915 year: 2021 ident: ref_30 article-title: Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101915 – volume: 8 start-page: e1249 year: 2018 ident: ref_41 article-title: Ensemble learning: A survey publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1249 – volume: 1522 start-page: 1159 year: 2013 ident: ref_57 article-title: Comparison of linear discriminant analysis and logistic regression for data classification publication-title: AIP Conf. Proc. doi: 10.1063/1.4801262 – volume: 521 start-page: 436 year: 2015 ident: ref_24 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 5 start-page: 180161 year: 2018 ident: ref_34 article-title: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions publication-title: Sci. Data doi: 10.1038/sdata.2018.161 |
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