Published Paper Details:

Skin cancer, especially melanoma, is a progressing public health burden. Treatment and survival rates improve dramatically when malignant melanoma skin cancer is detected at an early stage. In this study, we use many forms of artificial intelligence to complete the process of diagnosing melanoma. Picture scaling, hair removal, and contrast enhancement are all performed at the pre-processing phase of the input dermoscopic imaging. Next, Fuzzy C-Mean Clustering is used to extract lesions from the image once the data has been pre-processed. Extraction of characteristics from a picture involves eliminating color morphological and morphological transformation features. Furthermore, a Classification model called a Neural Network is used to classify features (NN). In this case, the NN model trained is modified using the Dragonfly Algorithm (MDA). Finally, the analysis is carried on standard datasets and proves the performance of the developed model from diverse performance metrics.