Classification of Skin Cancer with Deep Transfer Learning Method

Authors : Doaa Khalid Abdulridha ALSAEDİ, Serkan SAVAŞ

Pages : 202-210

Doi:10.53070/bbd.1172782

View : 19 | Download : 5

Publication Date : 2022-10-10

Article Type : Research Paper

Abstract :Skin cancer is a serious health hazard for human society. This disease is developed when the pigments that produce skin color become cancerous. Dermatologists face difficulties in diagnosing skin cancer since many skin cancer colors seem identical. As a result, early diagnosis of lesions insert ignore into journalissuearticles values(the foundation of skin cancer); is very crucial and beneficial in totally curing skin cancer patients. Significant progress has been made in creating automated methods with the development of artificial intelligence insert ignore into journalissuearticles values(AI); technologies to aid dermatologists in the identification of skin cancer. The widespread acceptance of AI-powered technologies has enabled the use of a massive collection of photos of lesions and benign sores authorized by histology. This research compares six alternative transfer learning networks insert ignore into journalissuearticles values(deep networks); for skin cancer classification using the International Skin Imaging Collaboration insert ignore into journalissuearticles values(ISIC); dataset. DenseNet, Xception, InceptionResNetV2, ResNet50, and MobileNet were the transfer learning networks employed in the investigation which were successful in different studies recently. To compensate for the imbalance in the ISIC dataset, the photos of classes with low frequencies are augmented. The results show that augmentation is appropriate for the classification success, with high classification accuracies and F-scores with decreased false negatives. With an accuracy rate of 98.35%, modified DenseNet121 was the most successful model against the rest of the transfer learning nets utilized in the study.
Keywords : Skin cancer, deep learning, ISIC, transfer learning, DenseNet