Harnessing Machine Learning for Enhanced Skin Cancer Detection and Classification

Cancer is one of the most dangerous diseases of all time. It is a disease in which the body's cells grow uncontrollably and spread to other parts of the body. It can grow in any region of the human body. There are majorly three types of cancers: - basal cell carcinoma, squamous cell carcinoma, and melanoma, out of which melanoma is the most severe and dangerous form of skin cancer, typically originating on the skin's surface. When identified early, the survival rate for melanoma patients reaches 96%, showing a drastic change in survival rate. Many skin lesions have many similarities when seen, making it hard to distinguish them for diagnosis. Therefore an automated system that can reliably distinguish the lesions is very necessary so that proper treatment can be given at the proper time. To solve this issue, we can use Machine Learning to build a model that will be able to scan the lesion image and predict the cancer type. Machine learning has been a powerful tool in classifying medical images for detecting various diseases, but a limitation of the machine learning approach is that it demands significant time and effort. Over the years, advancements in machine learning have helped a lot in the medical industry. Machine learning algorithms can efficiently analyze images, helping in the early detection and treatment of cancer and other deadly diseases. For the analysis of skin cancer, we implemented the Convolutional Neural Network (CNN) approaches with the TensorFlow and Keras frameworks, using the Adam optimizer to efficiently learn and classify skin cancer from images.