RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images.

Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study's goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups.

Ahalya RK ，Almutairi FM ，Snekhalatha U ，Dhanraj V ，Aslam SM ... -  《Scientific Reports》

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

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

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 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》

Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

Üreten K ，Erbay H ，Maraş HH 《-》