ADVANCED APPROACH FOR THE EARLY DETECTION OF MELANOMA

Often considered to be the deadliest and most fatal disease, melanoma commonly known as skin cancer has turned out to be a major threat worldwide. In early detection of melanoma, the chances are getting amplified to deaths rather than cases of survival. Early signs mostly turn up vague because the pre-diagnostic technological intersections lose the bond of difficulty to risks and rates of survival. Potential remedies including focused knowledge of the subject matter and in-depth solutions to the remedies can pave a proper path to cure deadly diseases and encounters. The study developed in this work through the implementation of Artificial Intelligence and deep learning can improve the investigation and case detection to detect lesions, extract proper features from images, classify images and implement the methodology of Convolutional neural networks, combining self-learning approaches makes the use of constrained data to cater the challenges in delivering the solution to uneven encounters. Henceforth, the created method acts as a major support to investigate several clinical (previous and ongoing) decisions and on selective attention paved to paths, this work aims to target the world to eradicate the upcoming revolutions that will highlight the improvements in melanoma detection.