Classification of healthy and unhealthy patients in dynamic infrared thermography of breasts using recurrent neural networks - Borralho, Antonio (Anais da Escola Regional de Computação Aplicada à Saúde (ERCAS); 2019: Anais da VII Escola Regional de Computação Aplicada à Saúde, 2019)
Breast cancer is the second most common type of cancer, being associated with a high mortality rate of women in the world. This paper proposes a methodology to classify patients as healthy or not healthy at images of infrared thermography dynamics, modeling the problem as time series. Techniques were used for image processing and analysis of time series through the use of recurrent neural networks such as long short-term memory. The data set is constituting 70 exams, 35 of them ill and 35 healthy patients. The proposed methodology has reached 92.3% of recall. Despite the result will not be close to the state-of-art, it is believed that this can be improved with the generation of synthetic series.