Deep Learning-Based Melanoma Detection with Optimized Features via Hybrid Algorithm

• Published in: International journal of image and graphics Vol. 23; no. 6
• Main Authors: Sukanya, S. T., Jerine, S.
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.11.2023; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Artificial neural networks; Clustering; Deep learning; Feature extraction; Image segmentation; Machine learning; Melanoma; Neural networks; Particle swarm optimization; Search algorithms; Skin cancer; Surface layers; Ultraviolet radiation; DCNN; Melanoma detection; FCM-based segmentation; PA-MSA; GLRM, LVP, LBP, LDP and LTR feature extraction
• ISSN: 0219-4678; 1793-6756
• DOI: 10.1142/S0219467823500560

Recently, there had been a massive group of people, who were being rapidly affected by melanoma. Melanoma is a form of skin cancer that develops on the skin’s surface layer. This is primarily caused due to excessive skin exposure to UV radiation and severe sunburns. Thus, the early detection of melanoma can aid us to cure it completely. This paper intends to introduce a new melanoma detection framework with four main phases viz. segmentation, feature extraction, optimal feature selection, as well as detection. Initially, the segmentation process takes place to the input skin image via Fuzzy C-Means Clustering (FCM) approach. From the segmented image ( Im seg ) , some of the features such as Gray Level Run Length Matrix (GLRM), Local Vector Pattern (LVP), Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Tetra Pattern (LTrP) are extracted. As the extracted features ( F ) suffered from the issue of “curse of dimensionality”, this paper utilizes optimization to select optimal features, which makes the detection more precise. As a novelty, a new hybrid algorithm Particle-Assisted Moth Search Algorithm (PA-MSA) is introduced that hybridizes the concept of Moth Search Algorithm (MSA) and Particle Swarm Optimization (PSO), respectively. For the classification process, the optimally chosen features ( F opt ) are fed as input, where Deep Convolution Neural Network (DCNN) is used. Finally, a performance-based comparative analysis is conducted among the proposed PA-MSA as well as the existing models with respect to various measures.