TY  - EJOU
AU  - Tahir, Javaria 
AU  - Naqvi, Syed Rameez 
AU  - Aurangzeb, Khursheed 
AU  - Alhussein, Musaed 

TI  - A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 70
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Skin lesion segmentation; image fusion; saliency detection; skin lesion classification; deep neural networks; transfer learning

DO  - 10.32604/cmc.2022.018949