Hybrid pigeon optimization-based feature selection and modified multi-class semantic segmentation for skin cancer detection (HPO-MMSS)

Abstract

Skin Cancer is among the most serious medical conditions in the worldwide, and curative results are better when detected early. Some of the challenges with conventional skin cancer detection methods, such as Genetic Algorithm, Particle Swarm Optimization, and U-Net-based segmentation models, include choosing the right characteristics and accurately detecting the skin lesions. This paper proposes a unique method that combines a Hybrid Pigeon Optimization Algorithm (HPOA) for feature selection with a Modified Multi-Class Semantic Segmentation (MMSS) model for lesion segmentation. The precise feature selection of the proposed HPOA improves the performance of the segmentation and classification models. Based on an enhanced U-Net architecture, the MMSS model uses skip connections and boundary detection techniques to improve segmentation accuracy. This approach integrates information from multiple feature spaces to produce a more informative structure for segmenting the skin lesions. The ISIC 2020 dataset count is considered as 2000, 4000, 6000, 8000, and 10000 for testing the proposed methodology. The experimental results with segmentation accuracy (ranging from 91% to 96%), precision (ranging from 89% to 94%), and recall (ranging from 88% to 94%) show that the proposed methodology gives a strong foundation for detecting skin cancer automatically.