Medical support platform for melanoma analysis and detection based on Federated Learning

Advances in computer science and medicine have led to the emergence of artificial intelligence as a key tool in the medical and scientific fields. Its application in the diagnosis and treatment of diseases, such as cancer, has proven to be fundamental in improving early detection and saving lives. This article presents a proposal based on Deep Learning to develop a model capable of detecting melanomas in the skin from clinical images. The aim is to provide doctors with a tool to support early identification of this type of cancer, considering additional factors such as sun exposure and the patient's skin tone. To optimize diagnostic accuracy and avoid information dispersion, a collaborative learning technique called Federated Learning is implemented. This technique allows models trained locally by doctors to be synchronized with a global model that will be updated periodically, ensuring continuous improvement of the system without compromising the privacy of patient data. In addition, a web application is presented to manage and process the information efficiently, making it easier for doctors to consult and analyze the results.