Medical infrared thermal image based fatty liver classification using machine and deep learning

Non-alcoholic fatty liver disease (NAFLD) causes accumulation of excess fat in the liver affecting people who drink little to no alcohol. Non-alcoholic steatohepatitis (NASH) is an aggressive form of fatty liver disease (inflammation in the liver), may progress to cirrhosis and liver failure. Liver function tests, ultrasound (US) and magnetic resonance imaging (MRI) are used to help diagnose and monitor liver disease or damage. In this study, the feasibility of medical infrared thermal imaging (MITI) in automatic detection of NAFLD was investigated, and 167 MITI images (44 positive) from 32 patients (7 positive) were evaluated using image processing and classification methods. Convolutional neural network (CNN) architectures and texture analysis methods were used in the feature selection phase. After feature selection and binary classification, the highest values from different setups for recall, f-score, specificity, accuracy, and area-under-curve (AUC) were 1.00, 1.00, 0.83, 1.0, 0.94, and 0.92, respectively. The highest values were achieved by CNN based methods on different datasets, however, texture analysis method performed lower. Here, it is shown that some of the CNN architectures have high potential on extracting features from thermal images. Finally, machine and deep learning approaches can be combined in detecting NAFLD using infrared thermal images.