Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Abstract

AbstractThe main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k‐means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classifiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k‐means algorithm, then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG‐16 and DenseNet‐121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG‐16 achieved an average accuracy of 97% compared to modified DenseNet‐121 which produced an average accuracy of 68% in classification of normal and OFP thermograms. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications.