Melhoria da tomada de decisão em serviços de saúde através de redes neurais convolucionais

Abstract

Artificial intelligence in production engineering, both in industry 4.0 and in services 4.0, has been a growing practice aimed at optimizing and automating various processes. In particular, in the health area, using tools such as Convolutional Neural Networks (RNCs) is an effective way to improve the accuracy of diagnosing pathologies and facilitating decision-making. A relevant example is the use of these networks in the diagnosis of melanoma, an aggressive form of skin cancer, which, if not detected and treated early, can spread quickly, significantly impacting public health, especially in countries like Brazil, due to high exposure to ultraviolet radiation. Melanoma represents a considerable portion of skin cancer mortality, and its advanced treatment has a high cost for health systems. This work aims to develop a Convolutional Neural Network model capable of predicting melanoma cancer diagnosis in the histopathological evaluation stage to improve decision-making in health services.