A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer

Abstract

Skin cancer is one of the most lethal and increasingly prevalent cancers in the world. Skin cancer develops when the epidermal (top layer of skin) cells divide abnormally, causing it to spread to other regions of the human body. Skin cancer exists in seven different varieties. The presence of malignant epidermal cells determines the type of skin cancer. Dermoscopy, spectroscopy, and imaging tests are primarily utilized to identify the malignancy. These procedures are expensive and prolonged. It may result in unfavorable effects such as bleeding, bruising, and infection as well. The narrow variances in multi class cancer pictures escalate the complexity of classification. Dermatologists confront challenges in the categorization of cancer types from images. Deep learning has resulted in a dramatic leap in disease identification. Deep learning models are capable of categorizing skin cancer more precisely than dermatologists. Several studies focused on pre-trained and hybrid models for categorizing the classes of skin cancer. In contrast to binary classification, the multi-class classification of skin cancer yielded an insignificant result for both deep learning and dermatologists. The proposed study employs varieties of deep learning and hybrid models to examine the performance of each model in categorizing the classes of cancer. The proposed CNN-SVM-LSTM hybrid model obtained the highest result compared to other models, with 87.15% accuracy, 87.42% precision, 87% recall, and 87.428% F1 score. To illustrate the overall comparison of the models, each model has been depicted through a classification report and a confusion matrix.