K. Kalyani¹, Sara A Althubiti², Mohammed Altaf Ahmed³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Neunggyu Han⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005 - 06 February 2023

Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification… More >

R. Thamizhamuthu^*, D. Manjula

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1147-1160, 2021, DOI:10.32604/cmc.2021.015503 - 22 March 2021

Abstract The deadliest type of skin cancer is malignant melanoma. The diagnosis requires at the earliest to reduce the mortality rate. In this study, an efficient Skin Melanoma Classification (SMC) system is presented using dermoscopic images as a non-invasive procedure. The SMC system consists of four modules; segmentation, feature extraction, feature reduction and finally classification. In the first module, k-means clustering is applied to cluster the colour information of dermoscopic images. The second module extracts meaningful and useful descriptors based on the statistics of local property, parameters of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of wavelet… More >