Melanoma Cancer Detection using Deep Learning Methods

Abstract

Abstract: One of the most hazardous types of skin cancer is melanoma because it spreads quickly and accounts for the majority of skin cancer fatalities because, if untreated, it is considerably more likely to migrate to other body regions. If melanomas are not found in their early stages, they cannot be treated.Therefore, melanoma treatment relies heavily on early detection. Melanoma is difficult to detect since it frequently looks benign and is misdiagnosed as such. Previous attempts to use neural networks to detect melanoma using the ABCD worked best with small datasets and low accuracy. The cloud technique requires a significant amount of time to train the dataset's images. The ensemble approach does not perform any image preprocessing, hence the final findings fall short of expectations.It is suggested to use an upgraded encoder-decoder network with separate encoder and decoder sub-networks connected by a number of skip paths. For the ISIC dataset and PH2 dataset, preprocessing strategies for pictures are suggested that aid to achieve high sensitivity and high specificity in lesion border segmentation to get greater accuracy compared to existing models.