{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:20:59Z","timestamp":1778692859088,"version":"3.51.4"},"reference-count":64,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":243,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Gene selection is critical for cancer diagnosis because the ability to discover specific biomarkers has a major impact on diagnostic accuracy. Traditional approaches frequently struggle with high\u2010dimensional genomic data, where irrelevant or redundant characteristics might impair machine learning algorithms. Despite advances in computational approaches, there is a gap in the optimization of deep learning models for gene selection, particularly in terms of selecting the best model architecture and hyperparameters. This paper addresses three critical challenges in genomic biomarker discovery for cancer diagnosis: (1) the high\u2010dimensional nature of gene expression data, (2) the need for biologically interpretable feature selection, and (3) the optimization of deep learning architectures for genomic analysis. We present a novel hybrid approach combining modified genetic algorithms with deep neural networks to overcome limitations of traditional methods in handling feature redundancy and computational complexity. Our methodology introduces three key innovations: a dynamic mutation operator that adapts to population diversity, multi\u2010objective optimization balancing classification accuracy with biological pathway relevance, and simultaneous co\u2010evolution of both gene subsets and neural network architectures. The proposed system achieves state\u2010of\u2010the\u2010art performance, with 99.1% accuracy, 98.9% AUC\u2010ROC, and 99.0% F1\u2010score on the ISIC 2020 dataset, while maintaining clinically relevant sensitivity (98.0%) and specificity (98.5%). Extensive validation across six benchmark datasets demonstrates consistent improvements over existing machine learning and deep learning techniques, particularly in handling rare cancer subtypes and low\u2010resolution images. Future research directions include: (1) integration of multi\u2010modal clinical data to enhance rare subtype detection, (2) development of federated learning frameworks for privacy\u2010preserving distributed analysis, and (3) creation of explainability tools to bridge the gap between computational feature selection and clinical interpretation. The results establish our evolutionary optimization approach as both a high\u2010performance diagnostic tool and a flexible framework for advancing precision oncology\u00a0research.<\/jats:p>","DOI":"10.1049\/ipr2.70186","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T14:51:04Z","timestamp":1756738264000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimized Gene and Image\u2010Based Feature Selection Using Modified Genetic Algorithms and Deep Learning for Predictive Skin Cancer Detection"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9871-3724","authenticated-orcid":false,"given":"Saadya Fahad","family":"Jabbar","sequence":"first","affiliation":[{"name":"Faculty of Education \u2010 Ibn Rushd for Human Science University of Baghdad Baghdad Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-0871","authenticated-orcid":false,"given":"M. A.","family":"Balafar","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9190-3605","authenticated-orcid":false,"given":"Ali Jameel","family":"Hashim","sequence":"additional","affiliation":[{"name":"The Communication and Media Commission Baghdad Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.16924"},{"issue":"2","key":"e_1_2_10_4_1","first-page":"150","article-title":"Enhanced Skin Cancer Detection Using Advanced Machine Learning Techniques","volume":"29","author":"Sharma S.","year":"2022","journal-title":"Journal of Computational Biology"},{"key":"e_1_2_10_5_1","first-page":"130","article-title":"Melanoma Recognition by a Deep Learning Convolutional Neural Network\u2014Performance in Different Melanoma Subtypes and Localisations","volume":"154","author":"Winkler J. K.","year":"2021","journal-title":"European Journal of Cancer"},{"key":"e_1_2_10_6_1","first-page":"45","article-title":"Deep Learning\u2010Based Gene Selection for Cancer Detection","volume":"18","author":"Zhang Y.","year":"2020","journal-title":"Journal of Bioinformatics and Computational Biology"},{"key":"e_1_2_10_7_1","first-page":"123","article-title":"Deep Learning for Gene Selection in Cancer Detection","volume":"5","author":"Wang Y.","year":"2023","journal-title":"Nature Machine Intelligence"},{"key":"e_1_2_10_8_1","first-page":"1234","article-title":"Ensemble Learning for Robust Gene Selection in Cancer Detection","volume":"68","author":"Li J.","year":"2021","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2023.1290696"},{"key":"e_1_2_10_10_1","first-page":"1","article-title":"Limitations of Support Vector Machines in High\u2010Dimensional Gene Selection","volume":"23","author":"Wang L.","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"e_1_2_10_11_1","first-page":"78","article-title":"Principal Component Analysis for Dimensionality Reduction in Gene Selection","volume":"30","author":"Chen X.","year":"2023","journal-title":"Journal of Computational Biology"},{"key":"e_1_2_10_12_1","first-page":"1","article-title":"Deep Learning\u2010Based Genetic Algorithm for Gene Selection","volume":"22","author":"Zhou L.","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_13_1","first-page":"45","article-title":"Generalizability of Gene Selection Models in Cancer Detection","volume":"151","author":"Liu H.","year":"2025","journal-title":"Journal of Cancer Research and Clinical Oncology"},{"key":"e_1_2_10_14_1","first-page":"567","article-title":"Hybrid GA\u2010SVM Model for Gene Selection and Classification","volume":"34","author":"Yang Y.","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_2_10_15_1","unstructured":"H.Liu Y.Zhang andX.Li \u201cHybrid Genetic Algorithm and Deep Neural Network for Gene Selection \u201dProceedings of the International Conference on Machine Learning (ICML)(2020) 1234\u20131245."},{"key":"e_1_2_10_16_1","unstructured":"A.Gupta S.Singh andR.Kumar \u201cGenetic Algorithm\u2010Based Gene Selection for Cancer Detection \u201dProceedings of the Genetic and Evolutionary Computation Conference (GECCO)(ACM 2020) 345\u2013352."},{"key":"e_1_2_10_17_1","first-page":"45","article-title":"Challenges in Genetic Algorithms for High\u2010Dimensional Gene Selection","volume":"28","author":"Singh S.","year":"2022","journal-title":"Journal of Heuristics"},{"key":"e_1_2_10_18_1","first-page":"100","article-title":"Particle Swarm Optimization for Gene Selection in Cancer Detection","volume":"64","author":"Kumar R.","year":"2021","journal-title":"Swarm and Evolutionary Computation"},{"key":"e_1_2_10_19_1","first-page":"1","article-title":"Interpretability of Machine Learning Models in Gene Selection","volume":"120","author":"Chen X.","year":"2024","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_2_10_20_1","first-page":"123","article-title":"Random Forest\u2010Based Gene Selection for Prostate Cancer Detection","volume":"38","author":"Smith J.","year":"2022","journal-title":"Bioinformatics"},{"key":"e_1_2_10_21_1","first-page":"89","article-title":"Artificial Bee Colony Algorithm for Gene Selection in Ovarian Cancer","volume":"17","author":"Jones M.","year":"2023","journal-title":"Swarm Intelligence"},{"key":"e_1_2_10_22_1","first-page":"567","article-title":"Hybrid Genetic Algorithm and Random Forest for Colorectal Cancer Detection","volume":"71","author":"Kim S.","year":"2024","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2024.3369944"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586\u2010017\u20101691\u20109"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-369"},{"key":"e_1_2_10_26_1","first-page":"151","article-title":"DCNN and its Applications in Dermatology","author":"Clark D.","year":"2022","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_10_27_1","first-page":"139","article-title":"FSLBODbneAlexnet: A Comprehensive Approach to Skin Lesion Classification","author":"King N.","year":"2023","journal-title":"Journal of Imaging"},{"key":"e_1_2_10_28_1","first-page":"128","article-title":"EfficientNetB0 vs. MobileNet_V2: A Comparison on the ISIC 2019 Dataset","author":"White G.","year":"2023","journal-title":"Journal of Computational Vision"},{"key":"e_1_2_10_29_1","first-page":"140","article-title":"MobileNet_V3 and SVM for Binary and Multiclass Skin Lesion Classification","author":"Harris E.","year":"2023","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_2_10_30_1","first-page":"162","article-title":"DenseNet\u2010121 for Robust Skin Lesion Analysis","author":"Walker P.","year":"2023","journal-title":"Journal of Medical Imaging and Health Informatics"},{"key":"e_1_2_10_31_1","first-page":"141","article-title":"Modified AlexNet for Enhanced Performance on HAM10000","author":"Adams H.","year":"2023","journal-title":"Journal of Digital Health"},{"key":"e_1_2_10_32_1","first-page":"164","article-title":"EfficientNet\u2010B6: A Study on Skin Lesion Classification","author":"Brown S.","year":"2023","journal-title":"International Journal of Biomedical Imaging"},{"key":"e_1_2_10_33_1","first-page":"152","article-title":"8\u2010Layer CNN for MNIST HAM10000 Multiclass Classification","author":"Wilson L.","year":"2023","journal-title":"Journal of Biomedical Engineering"},{"key":"e_1_2_10_34_1","first-page":"153","article-title":"A 16\u2010Layer CNN Approach to Skin Lesion Detection","author":"Moore I.","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3155774"},{"key":"e_1_2_10_36_1","first-page":"89","article-title":"Challenges in Implementing Hybrid Systems for Gene Selection","volume":"55","author":"Tan J.","year":"2024","journal-title":"Journal of Computational Science"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010020\u201005618\u2010x"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105457"},{"key":"e_1_2_10_39_1","article-title":"Feature\u2010Based Classification of Skin Lesions Using Deep Learning and Fuzzy Clustering","volume":"200","author":"Niemann D.","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"e_1_2_10_40_1","first-page":"2330","article-title":"A Novel Hybrid Method for Skin Cancer Detection Using Convolutional Neural Networks","volume":"12","author":"Patel G.","year":"2022","journal-title":"Applied Sciences"},{"issue":"3","key":"e_1_2_10_41_1","first-page":"1234","article-title":"Skin Lesion Classification With a Novel Feature Extraction Algorithm and Ensemble Learning","volume":"70","author":"Shen X.","year":"2023","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"e_1_2_10_42_1","first-page":"2123","article-title":"Random Forest Based Approach for Detecting Skin Cancers Using Large\u2010Scale Dataset","volume":"24","author":"Singh V.","year":"2023","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_10_43_1","article-title":"Multi\u2010Layer Perceptron for High\u2010Accuracy Skin Cancer Diagnosis Using Wavelet Features","volume":"147","author":"Zhang X.","year":"2023","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_2_10_44_1","article-title":"Enhanced Skin Lesion Classification Using SVM With Cross\u2010Spectrum and Cross\u2010Correlation Features","volume":"80","author":"Zhou J.","year":"2023","journal-title":"Medical Image Analysis"},{"key":"e_1_2_10_45_1","first-page":"17","article-title":"Generative Adversarial Networks for Robust Skin Cancer Classification","volume":"525","author":"Zhu Y.","year":"2023","journal-title":"Neurocomputing"},{"issue":"2","key":"e_1_2_10_46_1","first-page":"1875","article-title":"Two\u2010Stage Gaussian Process Method for Skin Cancer Classification","volume":"56","author":"Zhu X.","year":"2023","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"e_1_2_10_47_1","first-page":"67","article-title":"Generative Adversarial Network\u2010Based Deep Learning for Skin Cancer Detection","volume":"9","author":"Zou L.","year":"2023","journal-title":"Journal of Imaging"},{"key":"e_1_2_10_48_1","first-page":"175","article-title":"CI\u2010Net: A Novel Skin Cancer Detection Approach With Improved Segmentation","volume":"185","author":"Zhao H.","year":"2023","journal-title":"Pattern Recognition Letters"},{"issue":"2","key":"e_1_2_10_49_1","first-page":"23","article-title":"Automated Skin Lesion Analysis Using Support Vector Machine","volume":"7","author":"Abbas Q.","year":"2020","journal-title":"Journal of Medical Imaging"},{"issue":"6","key":"e_1_2_10_50_1","first-page":"1992","article-title":"Multiscale AM\u2010FM Analysis of Ultrasound RF Signals for Differentiation Between Benign and Malignant Breast Tumors","volume":"39","author":"Hon H.\u2010W.","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_2_10_51_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_10_52_1","first-page":"125","article-title":"MobileNet_V2: Applications in Skin Lesion Classification","volume":"38","author":"Mann E.","year":"2022","journal-title":"Journal of Medical Imaging"},{"key":"e_1_2_10_53_1","first-page":"149","article-title":"A Novel 56\u2010Layer CNN for Dermoscopic Image Analysis","author":"Hossny N.","year":"2022","journal-title":"International Journal of Computer Vision"},{"key":"e_1_2_10_54_1","first-page":"126","article-title":"Comparative Study of Mobilenet_V2 and Xception for Skin Lesion Classification","author":"Miles R.","year":"2022","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"e_1_2_10_55_1","first-page":"157","article-title":"Skin Lesion Detection Using Alexnet With Otsu Thresholding","author":"Ritchie A.","year":"2022","journal-title":"Journal of Dermatological Science"},{"key":"e_1_2_10_56_1","first-page":"127","article-title":"DenseNet_169 Performance on HAM10000 Dataset","author":"Doe J.","year":"2022","journal-title":"Medical Image Analysis"},{"key":"e_1_2_10_57_1","first-page":"142","article-title":"Hyperspectral Imaging for Skin Lesion Classification Using CNSVM","author":"Gomez C.","year":"2022","journal-title":"IEEE Access"},{"key":"e_1_2_10_58_1","first-page":"138","article-title":"Unit\u2010vise ResNet for Improved Skin Lesion Classification","author":"Jordan M.","year":"2022","journal-title":"Pattern Recognition Letters"},{"key":"e_1_2_10_59_1","first-page":"150","article-title":"Enhancing 15\u2010Layer CNN Performance for Skin Lesion Analysis","author":"Lee S.","year":"2022","journal-title":"Journal of Digital Imaging"},{"key":"e_1_2_10_60_1","unstructured":"R.GreenandS.Wilson \u201cResNet50 in Skin Lesion Classification: An Empirical Study \u201d inMedical Image Computing and Computer\u2010Assisted Intervention(2022):130\u2013142."},{"key":"e_1_2_10_61_1","unstructured":"M.Hosseini A. P.Patil andN. D.Sun \u201cEnhancing Skin Lesion Classification With Efficientnet and Custom Augmentation Techniques \u201d inIEEE Transactions on Medical Imaging42 no.3(2023):845\u2013855 https:\/\/doi.org\/10.1109\/TMI.2023.3298098."},{"key":"e_1_2_10_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3102846"},{"key":"e_1_2_10_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104454"},{"key":"e_1_2_10_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16594-1"},{"key":"e_1_2_10_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10139-6"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70186","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70186","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:42:08Z","timestamp":1778283728000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70186"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70186","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-04-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70186"}}