Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists
Ibrahim, S., Amin, K., Ibrahim, M. (2023). Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists. EKB Journal Management System, 10(3), 126-133. doi: 10.21608/ijci.2023.236366.1141
Sally Ibrahim; Khalid M. Amin; Mina Ibrahim. "Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists". EKB Journal Management System, 10, 3, 2023, 126-133. doi: 10.21608/ijci.2023.236366.1141
Ibrahim, S., Amin, K., Ibrahim, M. (2023). 'Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists', EKB Journal Management System, 10(3), pp. 126-133. doi: 10.21608/ijci.2023.236366.1141
Ibrahim, S., Amin, K., Ibrahim, M. Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists. EKB Journal Management System, 2023; 10(3): 126-133. doi: 10.21608/ijci.2023.236366.1141

Enhanced Skin Cancer Classification using Pre-Trained CNN Models and Transfer Learning: A Clinical Decision Support System for Dermatologists

IJCI. International Journal of Computers and Information
Article 18, Volume 10, Issue 3, November 2023, Page 126-133  XML PDF (525.86 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.236366.1141
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Authors
Sally Ibrahim email 1; Khalid M. Aminorcid 2; Mina Ibrahimorcid 3
1Information Technology Dept faculty of Computers and Information, Menoufia University Kafr EL-Sheikh, Egypt
2Information technology dept., Faculty of computers and information, Menofia university
3Department of Information Technology Faculty of Computers and Information, Menoufia University, Egypt
Abstract
Skin cancer is one of the fatal illnesses that cause a high percentage of deaths worldwide. The treatment of skin cancer depends critically on early detection of the disease. Dermatologists face several difficulties in the diagnostic process to address this issue. This study aims to improve the classification accuracy of seven types of skin tumors by using transfer learning on pre-trained Convolutional Neural Network models. The modified models, including EfficientNetB3, EfficientNetB0, and ResNet50, were trained on a dataset of 10,015 dermatoscopic images. The dataset was augmented using data augmentation techniques, and the class imbalance problem was addressed. This study offers a promising approach for clinicians to make informed judgments about patient diagnoses. Our results showed the superiority of the modified models over the official models. Our best model, the modified EfficientNetB3, achieved an accuracy of 91.6%, Precision of 83%, F1-Score of 88 %, and Recall of 94% on the HAM10000 dataset.
Keywords
Skin cancer; Transfer Learning; EfficientNet; Deep Learning; HAM10000 dataset
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