A comparative study of fourteen deep learning networks for multi skin lesion classification (MSLC) on unbalanced data

Abstract

Among various types of skin diseases, skin cancer is the deadliest form of the disease. This paper classifies seven types of skin diseases: Actinic keratosis and intraepithelial carcinoma, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanoma, Melanocytic type, and Vascular lesions. The primary objective of this paper is to evaluate the performance of these deep learning networks on skin lesion images. The lesion classification is implemented through transfer learning on fourteen deep learning networks: AlexNet, GoogleNet, ResNet50, VGG16, VGG19, ResNet101, InceptionV3, InceptionResNetV2, SqueezeNet, DenseNet201, ResNet18, MobileNetV2, ShuffleNet and NasNetMobile. The dataset used for these experiments are from ISIC 2018 of about 10,154 images. The results show that DenseNet201 performs best with 0.825 accuracy and improves skin lesion classification under multiple diseases. The proposed work shows the various parameters, including the accuracy of all fourteen deep learning networks, which helped build an efficient automated classification model for multiple skin lesions.