The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.

Background: Colorectal cancer (CRC) is a major contributor to cancer mortality on a global scale, with polyps being critical precursors. The accurate classification of colorectal polyps (CRPs) from colonoscopy images is essential for the timely diagnosis and treatment of CRC. Method: This research proposes a novel hybrid model, CRP-ViT, integrating ResNet50 with Vision Transformers (ViTs) to enhance feature extraction and improve classification performance. This study conducted a comprehensive comparison of the CRP-ViT model against traditional convolutional neural networks (CNNs) and emerging quantum neural networks (QNNs). Experiments were conducted for binary classification to predict the presence of polyps and multi-classification to predict specific polyp types (hyperplastic, adenomatous, and serrated). Results: The results demonstrate that CRP_QNN-ViT achieved superior classification performance while maintaining computational efficiency. CRP_QNN-ViT achieved an accuracy of 98.18% for training and 97.73% for validation on binary classification and 98.13% during training and 97.92% for validation on multi-classification tasks. In addition to the key metrics, computational parameters were compared, where CRP_QNN-ViT excelled in computational time. Conclusions: This comparative analysis reveals the potential of integrating quantum computing into medical image analysis and underscores the effectiveness of transformer-based architectures for CRP classification.

Objectives: The aim of this study was (i) to design and develop a portable BCA device for measuring body composition parameters such as body weight, body fat (BF) %, total body water (TBW), fat-free mass (FFM), muscle mass (MM), and bone mass (BM); (ii) to validate the developed portable BCA with the Tanita MC 980 MA BCA device. Methods: For this current study, two hundred healthy and obese subjects, whose ages ranged from 8 to 12 years (8.4 ± 1.7), were considered. Results: The highest percentage difference between the two study groups was found to be in BFat (50.39%), followed by body mass index (BMI) (41.73 kg), FFM (38.32 kg), and MM (37.89 kg), and this was found to be statistically significant. The results obtained from the designed prototype of the body composition analyzer were validated using Tanita MC 980MA BCA. The overall error% was calculated as ±3% for measuring the different body composition parameters. Conclusions: Due to its low standard error and high overall accuracy, the BCA prototype demonstrates the potential to be a dependable instrument for evaluating and tracking the body composition of children.

Purpose: The precise diagnosis of Parkinson’s Disease (PD) continues to pose a considerable barrier in clinical neurology. This work investigates the capability of sophisticated deep learning architectures to improve Parkinson’s disease identification via Magnetic Resonance Imaging (MRI) scans, aiming to improve diagnostic sensitivity and specificity. Method: This research implements an automated PD classification framework utilizing deep learning models to analyze real-time MRI scans. CNN models and Transformers that leverages the inherent spatial information within MRI scans for automated feature extraction and classification of PD was implemented. The comparative analysis of diverse architectures aimed to discern the optimal feature extraction and classification strategies for distinguishing PD-affected brains from healthy controls was performed. K-fold cross-validation was conducted to evaluate the reliability of dataset partitioning and to ensure model generalizability. Results: The QNN demonstrated superior performance during training and validation with accuracy of 0.9851 and 0.9731 respectively. Among the Transformer-based architectures, the Swin Transformer achieved a training and validation accuracy of 0.9712 & 0.9673. In the category of CNN-based models, ResNet50 exhibited the highest accuracy, attaining 0.9322 during training and 0.9192 during validation. The K-fold cross-validation results corroborated the stability and consistency of the model performances with the 80:20 data split, reinforcing the robustness of the proposed approach. Furthermore, to assess the generalizability of the best-performing model, an external validation was conducted using the Parkinson’s Progression Markers Initiative (PPMI) dataset. The results demonstrated comparable performance trends, thereby confirming the adaptability of the DL model to independent datasets. Conclusion: This study’s findings highlight the capability of deep learning-driven neuroimaging analysis in facilitating automated and precise PD diagnosis. This investigation provides a scalable and clinically viable AI framework that enhances diagnostic accuracy, thereby supporting clinicians in disease detection and improving patient management strategies.