An Intelligent Dermoscopic Image Analysis Framework for Skin Cancer Detection Using Enhanced Metaheuristic-Optimised Support Vector Machines

Abstract

Skin cancer is among the most common and lethal malignancies globally, with its high mortality rate primarily due
to delayed diagnosis and insufficient early detection. Despite recent advancements, current computer-aided diagnostic techniques frequently exhibit restricted classification accuracy, high computational complexity, and ineffective hyperparameter optimisation, thereby limiting their efficacy in practical clinical settings. This study aims to create efficient and robust intelligent models for precise skin cancer detection and classification by improving optimisation-driven machine learning techniques. The study proposes two enhanced multi-class Support Vector Machine (SVM)-based frameworks to achieve this objective: MCMLMBSVM-EAO and MCMLMBSVM- EHHO. The study aims to: (i) improve dermoscopic image quality via preprocessing techniques such as resizing, grayscale conversion, contrast enhancement, and noise reduction; (ii) accurately segment lesion regions using the Sobel edge detection method; (iii) extract discriminative colour, shape, and texture features using Colour Moments and Gray Level Co-occurrence Matrix (GLCM); and (iv) enhance SVM classification efficacy through sophisticated hyperparameter optimisation. To address intrinsic optimisation challenges, the Aquila Optimiser (AO) and Harris Hawks Optimiser (HHO) are methodically refined with innovative mechanisms to augment convergence speed, search efficacy, and solution robustness. The proposed models are assessed using the benchmark HAM10000 dermoscopic dataset. Experimental findings indicate that the MCMLMBSVM-EAO and MCMLMBSVM-EHHO models attain classification accuracies of 98.45% and 98.67%, respectively, surpassing numerous leading methodologies. The findings underscore the efficacy, resilience, and generalisability of the proposed frameworks, rendering them appropriate for dependable and efficient automated dermatological diagnosis, especially in resource-limited settings.