@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}
}