Examining the Performance of Melanoma Classification using Superpixel Segmentation: A Comparative Analysis

Skin cancer, characterized by the abnormal growth of skin cells, is a severe and prevalent condition. Despite the advancement in digital diagnosis techniques, existing skin cancer detection methods often fail to achieve satisfactory performance in melanoma identification in dermoscopy imaging. This study presents a new melanoma classification algorithm that utilizes a superpixel-based segmentation technique, simple linear iterative clustering, and transfer learning-based convolutional neural networks to achieve the objective. A key innovation of our methodology is the introduction of a novel approach for efficiently merging superpixels, that enhances the quality of segmentation resulting in better classification performance. Following the segmentation phase, several convolutional neural networks have been utilized for feature extraction from segmented images and classification. The proposed method shows an enhanced performance in melanoma classification which is 91.67%, 95.33%, 91.23%, and 90.48% in terms of accuracy, precision, recall and F1-score, respectively. Our results indicate that the superpixel segmentation technique considerably enhances the classification models’ accuracy compared to k-means segmentation methods. A comparative analysis between the proposed method and several state-of-the-art methods in the field has also been presented in the context.