Classificação de Doença Hepática Gordurosa Não Alcoólica em Imagens Térmicas usando Temporal Convolutional Networks

• Marcos Vinícius de Sousa Farias UFMA
• Italo Francyles Santos da Silva UFMA
• Aristófanes Corrêa Silva UFMA
• Henrique Manoel de Araújo Martins Filho Alchimia
• Anselmo Cardoso Paiva UFMA

DOI: https://doi.org/10.5753/sbcas.2023.229802

Abstract

Among the existing liver pathologies, Non-Alcoholic Fatty Liver Disease (NAFLD) is the one that affects the largest portion of the world’s population, approximately 2 billion people. NAFLD has considerable chances of progressing to more severe clinical conditions, such as fibrosis and liver cirrhosis, representing serious risks to the lives of patients. Thus, it is vital that its detection be done in an agile, accurate and preferably non-invasive way, with the use of thermal images being a highly supported method in this area. The present work presents the development of a model for classifying NAFLD from time series in thermographs. Temporal Convolutional Networks combined with image processing strategies were used in the composition of the proposal.

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