Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique.

The aims and objectives of the study were to i) perform image segmentation using a color-based k-means clustering algorithm and feature extraction using binary robust invariant scalable key points (BRISK), maximum stable extremal regions (MSER), features from accelerated segment test (FAST), Harris, and orientated FAST and rotated BRIEF (ORB); ii) compare the performance of classical machine learning techniques such as LogitBoost, Bagging, and SVM with a quantum machine learning technique. For the proposed study, 240 hand thermal images were acquired in the dorsal view and ventral view of both the right and left-hand regions of RA and normal subjects. The hot spot regions from the thermograms were segmented using a color-based k-means clustering technique. The features from the segmented hot spot region were extracted using different feature extraction methods. Finally, normal and RA groups were categorized using LogitBoost, Bagging, and support vector machine (SVM) classifiers. The proposed study used two testing methods, such as 10-fold cross-validation and a percentage split of 80-20%. The LogitBoost classifier outperformed with an accuracy of 93.75% using the 10-fold cross-validation technique compared to other classifiers. Also, the quantum support vector machine (QSVM) obtained a prediction accuracy of 92.7%. Furthermore, the QSVM model reduces the computational cost and training time of the model to classify the RA and normal subjects. Thus, thermograms with classical machine learning and quantum machine learning algorithms could be considered a feasible technique for classifying normal and RA groups.

Ahalya RK ，Snekhalatha U ，Dhanraj V 《JOURNAL OF THERMAL BIOLOGY》

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.

Kesavapillai AR ，Aslam SM ，Umapathy S ，Almutairi F ... -  《Diagnostics》

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques.

Ahalya RK ，Umapathy S ，Krishnan PT ，Joseph Raj AN ... -  《-》

2-Stage classification of knee joint thermograms for rheumatoid arthritis prediction in subclinical inflammation.

Bardhan S ，Bhowmik MK 《-》

Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis.

The aim of the study was (1) to perform an automated segmentation of hot spot regions of the hand from thermograph using the k-means algorithm and (2) to test the potential of features extracted from the hand thermograph and its measured skin temperature indices in the evaluation of rheumatoid arthritis. Thermal image analysis based on skin temperature measurement, heat distribution index and thermographic index was analyzed in rheumatoid arthritis patients and controls. The k-means algorithm was used for image segmentation, and features were extracted from the segmented output image using the gray-level co-occurrence matrix method. In metacarpo-phalangeal, proximal inter-phalangeal and distal inter-phalangeal regions, the calculated percentage difference in the mean values of skin temperatures was found to be higher in rheumatoid arthritis patients (5.3%, 4.9% and 4.8% in MCP3, PIP3 and DIP3 joints, respectively) as compared to the normal group. k-Means algorithm applied in the thermal imaging provided better segmentation results in evaluating the disease. In the total population studied, the measured mean average skin temperature of the MCP3 joint was highly correlated with most of the extracted features of the hand. In the total population studied, the statistical feature extracted parameters correlated significantly with skin surface temperature measurements and measured temperature indices. Hence, the developed computer-aided diagnostic tool using MATLAB could be used as a reliable method in diagnosing and analyzing the arthritis in hand thermal images.

Snekhalatha U ，Anburajan M ，Sowmiya V ，Venkatraman B ，Menaka M ... -  《-》