Computer aided diagnosis of obesity based on thermal imaging using various convolutional neural networks

Abstract Objectives The study aims at the following: i) To construct a custom Deep Learning Network for classifying the thermal images of abdomen, forearm and shank regions into obese and normal cases ii) To compare the performance of the proposed CNN with some of the state-of-the-art pre-trained CNN and Machine Learning models in detection of obesity. Methods Fifty healthy subjects along with fifty other age cum sex matched obese subjects were included in the study. The mean skin surface temperature was measured in abdomen, shank and forearm region for normal and obese subjects. After data augmentation, the images are fed to the proposed CNN and pre-trained networks for training, validation and classification of normal and obese thermograms. Results Among the ROI studied, the abdomen region exhibited a high temperature difference of 4.703 % between the normal and obese compared to other regions. The proposed custom network-2 provided an overall accuracy of 92 %, area under the curve (AUC) value of 0.948 whereas the pre-trained model VGG16 net produced an accuracy of 79 % and AUC value of 0.90 for discrimination into obese and normal thermograms. Conclusions Hence, the deep learning system based on custom CNN provided a reliable classification performance to identify the occurrence of obesity in test subjects. The experimental analysis showed that custom CNN network-2 provided a commendable degree of accuracy in classification of normal and obese subjects from the thermal images. Thus, the trained Custom-2 CNN model can be used for computer aided screening of test subjects for obesity detection.