SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

Abstract

Skin cancer has become one of the most common diseases due to the depletion of the ozone layer and the decrease in its protection. Detection and classification of skin cancer in the early stages of its development allows patients to receive appropriate treatment quickly. In this article, a modified CNN framework based on transfer learning is proposed for the classification of skin lesions from skin dermoscopy images. In the proposed framework, pretrained CNN architectures are used. VGG16, ResNet50, DeneNet121, MobileNet, and Xception models were pre-trained using ImageNet images and training weights. In the study training and tests were performed on the HAM10000 skin lesions data set. The classification accuracy of the modified DenseNet121, VGGNet16, ResNet50, MobileNet, and Xception models were calculated as 94.29%, 93.28%, 87.10%, 83.10%, and 80.05% respectively. It was observed that the accuracy success of the proposed transfer learning framework in skin lesion type classification surpasses classical deep learning architectures.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…Al-Tuwaijari, et al [43] used three pretrained deep learning models namely DenseNet121, VGG19, and an improved ResNet152 for classifying skin cancer from dermoscopic images. Tasar [44] developed SkinCancerNet, employing modified CNN frameworks based on transfer learning for classifying skin lesions. Magdy, et al [45] proposed two methods for detecting and classifying dermoscopic images into benign and malignant tumors, utilizing optimized AlexNet.…”