Abstract
Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white popula-tions. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject's survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we pro -pose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two methods simultaneously; one is the weighted visual saliency-based method, and the second is improved HDCT based saliency estimation. The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region. The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer learning. The simulation results show improved performance compared to several existing methods.