Effective Deep Learning Network Model for Multi-Class Skin Cancer Classification

Abstract

Skin cancer is a prevalent condition that may affect anybody, regardless of age. To lower the fatality rate by catching diseases in their earliest stages, automated skin cancer detection is required. To lower the fatality rate by catching diseases in their earliest stages, automated skin cancer detection is required. In recent years, skin cancer screening and detection have become more popular due to the imaging-based computer-aided diagnosis (CAD) approach. To identify and classify skin lesions, this research developed an automated integrative deep-learning network model. The hair is removed using a dull razor approach, and the noise is removed using an average median filter during image pre-processing. The afflicted lesion regions are found in the dermoscopic images using the Otsu thresholding approach and mathematical morphology. From the segmented lesions, the significant features are extracted using improved capsule network (Improved CapsNet). Finally, the enhanced optimized probabilistic neural network (EOPNN) is used for skin lesion classification. The improved artificial jelly optimization (IAJO) algorithm enhances the EOPNN classifier. A benchmark ISIC dataset is used to validate the proposed EOPNN-IAJO approach. The proposed structure has achieved encouraging results for several metrics with 99.23% specificity, 99.02% sensitivity, and 99.53% accuracy.