An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Abstract

Early detection of melanoma is crucial in preventing death from this fatal skin cancer. Therefore, it would be valuable to develop a method that facilitates this process. The diagnosis of melanoma typically involves an invasive form of testing called a biopsy, as well as non-invasive intelligent approaches to diagnosis. In the present study a recent research, a novel approach has been developed for the optimal detection of melanoma cancer. The method uses reinforcement learning for segmenting the skin regions, followed by the extraction and selection of useful features using the Enhanced Fish Migration Optimizer (EFMO) algorithm. The outcomes get categorized on the basis of an optimized SVM on the basis of the EFMO algorithm. The recommended approach has been certified by applying it to the SIIM-ISIC dataset of Melanoma and comparing it with 12 other approaches. Simulations illustrated that the proposed method delivered the finest values compared to the others.

Keywords

• Melanoma detection
• Feature selection
• Reinforcement learning
• Enhanced fish migration optimizer (EFMO)
• Feature extraction
• Support vector machine