Multi-Skin disease classification using hybrid deep learning model.

Abstract

Among the many cancers that people face today, skin cancer is among the deadliest and most dangerous. As a result, improving patients' chances of survival requires skin cancer to be identified and classified early. Therefore, it is critical to assist radiologists in detecting skin cancer through the development of Computer Aided Diagnosis (CAD) techniques. The diagnostic procedure currently makes heavy use of Deep Learning (DL) techniques for disease identification. In addition, skin lesion extraction and improved classification performance are achieved through Region Growing (RG) based segmentation. At the outset of this study, noise is reduced using an Adaptive Wiener Filter (AWF), and hair is removed using a Maximum Gradient Intensity (MGI). Then, the best RG, which is the result of integrating RG with the Modified Honey Badger Optimiser (MHBO), does the segmentation. Finally, several forms of skin cancer are classified using the DL model MobileSkinNetV2. The experiments were conducted on the ISIC dataset and the results show that the accuracy and precision were improved to 99.01% and 98.6%, respectively. In comparison to existing models, the experimental results show that the proposed model performs competitively, which is great news for dermatologists treating cancer.