@Article{cmc.2022.018949, AUTHOR = {Javaria Tahir, Syed Rameez Naqvi, Khursheed Aurangzeb, Musaed Alhussein}, TITLE = {A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {70}, YEAR = {2022}, NUMBER = {2}, PAGES = {3235--3250}, URL = {http://www.techscience.com/cmc/v70n2/44625}, ISSN = {1546-2226}, ABSTRACT = {Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. 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 propose 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.}, DOI = {10.32604/cmc.2022.018949} }