Melanoma Skin Cancer Disease Classification using Deep Convolutional Neural Network

Melanoma represents a highly aggressive variant of cutaneous malignancy, thereby constituting a significant risk to public health. The cornerstone of efficacious treatment resides in the prompt and precise identification of the disease. Conventional methodologies, including visual examination and dermatoscopic, exhibit a degree of subjectivity and are suspectable to inaccuracies. Recent advancements in deep learning, particularly convolutional neural networks (CNN)s have already demonstrated notably promising outcomes in the pursuit of automated melanoma classification. In the present investigation, a novel deep learning architecture3 utilizing CNNs for the classification of melanoma images in the context of benign skin lesions has been proposed. The effective application of CNNs to features extracted from images can facilitate predictions employing the suggested model. Based on the experimental findings associated with the proposed model. There exists a potential to classify melanoma with greater efficiency compared to traditional techniques.