TY  - JOUR
T1  - Classification of Skin Cancer with Deep Transfer Learning Method
TT  - Classification of Skin Cancer with Deep Transfer Learning Method
AU  - Savaş, Serkan
AU  - Al-saedi, Doaa Khalid Abdulridha
PY  - 2022
DA  - October
Y2  - 2022
DO  - 10.53070/bbd.1172782
JF  - Computer Science
JO  - JCS
PB  - Ali KARCI
WT  - DergiPark
SN  - 2548-1304
SP  - 202
EP  - 210
VL  - IDAP-2022 : International Artificial Intelligence and Data Processing Symposium
LA  - en
AB  - 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 (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 (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 (deep networks) for skin cancer classification using the International Skin Imaging Collaboration (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.
KW  - Skin cancer
KW  - deep learning
KW  - ISIC
KW  - transfer learning
KW  - DenseNet
N2  - 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 (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 (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 (deep networks) for skin cancer classification using the International Skin Imaging Collaboration (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.
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UR  - https://doi.org/10.53070/bbd.1172782
L1  - https://dergipark.org.tr/en/download/article-file/2640169
ER  -