WOA-Driven optimal feature selection from a fused deep and hand-crafted feature set for skin melanoma classification

One of the fastest globally proliferating malignancies is skin melanoma cancer. Thus, by using dermoscopy images, researchers hope to develop a reliable technique for identifying skin lesions as malignant or benign, which they can use in clinical practice further. This paper proposes a novel way of identifying skin melanoma by fusion of hand-crafted features together with deep learning features followed by feature selection step. A custom dataset stated on Kaggle as the Melanoma Skin Cancer Dataset comprising of 10605 dermoscopic images of malignant as well as benign lesions is used, on which various preprocessing steps are performed followed by feature extraction and fusion. The hand-crafted GLCM, GLRLM, SFTA features are extracted and combined with deep learning features from DenseNet201. The features are then selected from fused feature set using bio-inspired based Whale Optimization Algorithm (WOA). Five cross-validation protocols (k = 2, 3, 4, 5, 10) are carried out on the dataset. The Kernel-SVM classifier is used for the classifying the skin lesions as benign or malignant. Under 10-fold cross-validation, the proposed model achieves the mean accuracy of 99.28%, sensitivity of 99.28% and specificity of 99.27% outperforming individual feature models and PSO-based feature selection. A statistical significance testing using paired t-test confirms that the improvements over comparative techniques are significant. Further validation on the external HAM10000 dataset demonstrates strong generalization capability achieving 96.52% accuracy. The proposed fused features WOA-SVM framework provides an efficient solution for melanoma classification.