Innovating Skin Cancer Detection with Transfer Learning, Feature Fusion, Efficient Data Management, Data Augmentation and Interdisciplinary Expertise for Better Outcomes

Abstract

There is a growing acceptance of the use ofArtificial Intelligence (AI) in the use of healthcare,including in dermatology.AI is better at recognizingimages and learning patterns and due to this, i hasgrown into a powerful tool that can sometimes be moreaccurate than people and this has been demonstratedin recent studies showcasing the potential of AI.According to the World Cancer Research FundInternational, the global incidence of skin cancer maybe lesser than it is because of the challenges in datacollection, different types of skin cancer, interpretationof the results and demographics.Early detection and diagnosis in the majority of casessaves life. This has been proven overtime with mostNon-communicable diseases NCDs and this is nodifferent with skin cancer. This is where theapplication of AI in the diagnosis comes to play.In this study, we discussed the application of AI inMelanoma diagnosis. The use of machine learningmethods like Reinforcement Learning (RL), SupportVector Machines (SVM), K-Nearesr Neighbour (KNN)and Deep learning models like Convolutional NeuralNetwork (CNN), transfer learning and a combinationof both models were explored and showed significantaccuracies in Melanoma diagnosis compared to thetraditional methods. We also looked at the economicimpact of AI in healthcare, its cost-effectiveness andpublic perception of using AI.This study further emphasises the growing importanceof AI in healthcare and its integration into Melanomadiagnosis. It also explored the importance of AI inassisting dermatologists in the early detection of skincancer thereby saving lives.AI has the impact to makeour medical decision making easier, faster and withbetter accuracy and precision thereby increasingaccess to care.