AI-Powered Skin Disease Detection Using Adaptive Particle Swarm Intelligent Optimization and Hyper-Convolutional Neural Networks

Abstract

In recent medical research, skin cancer has emerged as one of the most prevalent and fatal cancers globally. Previous studies have faced challenges in detecting skin cancer early due to the complexity of identifying specific skin diseases, segmenting affected areas, and selecting relevant features. To address these limitations, this study proposes a novel AI-powered enhanced skin disease detection system that applies an Adaptive Particle Swarm Intelligent Optimization (APSIO) in conjunction with a Hyper-Convoluted Intra-Capsuled Neural Network (HCI-CNN). In image processing, a Gaussian Wavelet Spectral Filter is initially used to preprocess the input dataset of skin-cancer images. This filter is used to standardize the skin layer of the pixel. After preprocessing, the method applies Slice Fragment Window Segmentation (SFWS) to divide the image into several clusters, focusing on the specified area affected by the disease. Next, Adaptive Particle Swarm Intelligent Optimization (APSIO) is applied for feature selection. APSIO is an optimization metaheuristic algorithm that optimizes the selection of relevant features from the segmented image. After removing evaluated and non-effective features, YOLO extracted features are passed through an HCI-CNN classifier to efficiently characterize high-level spatial hierarchies and relations of features in the feature space using hyper-convolutional operations and capsule representations. This paper analyzed the clinical images of individuals along with the dataset images. The output gain improved Accuracy to 97%, precision to 96.52%, recall to 96.55%, and F1-score to 96.93%, while simultaneously minimizing false positives and total time complexity.