Hybrid optimization based deep neuro fuzzy network for skin cancer detection

Abstract

SummaryMelanoma is the most prevalent type of skin cancer, affecting skin surface cells. The risk features of the disease are minimized by finding the disease at time or in the initial stage. Even through, various skin cancer detection methods are developed, detecting the disease using discriminative features still results a challenging task. An efficient detection strategy based on the proposed Lion Cat Swarm Optimization‐based Deep Neuro Fuzzy Network (LCSO‐based DNFN) is created to identify skin cancer in its early stages. The LCSO is the integration of the lion optimization algorithm (LOA) and cat swarm optimization (CSO) algorithm. To make treatment measure at right time increases the probability of survival and so detecting the disease using deep learning classifier is performed with hybrid optimization algorithm. The segmentation result is effective through the fusion model and it ensures to achieve accurate detection process. Rather than extracting the features, the proposed method generates optimal result through data augmentation. The performance of the proposed LCSO‐based DNFN is determined using skin disease dataset, and the introduced approach obtained outstanding performance with the measures of accuracy, sensitivity, and specificity with the values of 93.10%, 92.95%, and 92.87%, respectively.