Ranet: A Custom Cnn Model And Quanvolutional Neural Network For The Automated Detection Of Rheumatoid Arthritis In Hand Thermal Images

This article presents a deep learning and quantum computing-based automated diagnostic approach for Rheumatoid Arthritis (RA) in hand thermal imaging. The 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 Machine Learning (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.