Early Detection of Arthritis Using Convolutional Neural Networks and Explainable AI

Abstract

Arthritis is a prevalent and debilitating musculoskeletal disorder that significantly impairs mobility, joint function, and overall quality of life for millions of individuals across the globe. The condition is characterized by chronic joint inflammation, cartilage degradation, stiffness, and persistent pain, often leading to long-term disability and increased healthcare dependency. As the global population continues to age, the healthcare impact and economic burden of arthritis are expected to rise substantially. Early detection and precise classification of arthritis are therefore essential for initiating effective treatment, slowing disease progression, improving long-term prognosis, and reducing healthcare system strain. However, traditional diagnostic approaches such as physical examination and manual interpretation of radiographic images by clinicians are often subjective, time-consuming, and prone to inter-observer variability. These limitations emphasize the urgent need for intelligent, reproducible, and scalable computer-aided diagnostic systems. This study proposes a novel deep learning-based framework that incorporates Explainable Artificial Intelligence (XAI) techniques to automatically classify the severity of arthritis using X-ray imaging data. Specifically, the research investigates and compares the performance of six widely recognized convolutional neural network (CNN) architectures: EfficientNetB5, ResNet50, InceptionV3, DenseNet121, VGG16, and MobileNetV2. These architectures were systematically trained and validated on a curated dataset of arthritis X-ray images, with the aim of identifying the most robust and efficient model for classifying different stages of arthritis severity with high diagnostic precision and generalizability. Among the evaluated models, the VGG16 architecture demonstrated the highest classification accuracy, achieving a performance of 96.17%, making it a strong candidate for clinical integration. DenseNet121 followed with an accuracy of 91.35%, while EfficientNetB5, InceptionV3, and ResNet50 each delivered competitive results within the range of 88% to 89%. MobileNetV2, although computationally lighter and more efficient in terms of processing speed, exhibited the lowest performance with an accuracy of 85.43%. These findings reveal that deeper and well-optimized CNN architectures tend to offer superior results for medical image classification tasks, particularly when image features are subtle and require high-level abstraction. To enhance the transparency and interpretability of the classification outcomes, the study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) into the system pipeline. Grad-CAM generates visual heatmaps that identify and highlight the most influential regions within the X-ray images that guided the model’s predictions. This visual interpretability is essential for clinical adoption, as it allows healthcare professionals to validate and understand the AI’s decision-making process, thereby fostering trust, transparency, and accountability. The outcomes of this study illustrate the transformative potential of AI-powered tools in medical diagnostics. By automating the identification and classification of arthritis severity, the proposed framework can significantly reduce diagnostic delays, improve diagnostic consistency, and support more timely and informed treatment decisions. Future research directions include expanding the dataset to incorporate more diverse imaging samples, refining model architectures for enhanced real-time deployment, and integrating multimodal clinical data such as MRI scans, blood biomarkers, and patient history to further elevate diagnostic accuracy and support holistic arthritis management strategies.

Keywords: Arthritis, Deep Learning, Convolutional Neural Networks, X-ray Imaging, Explainable Artificial Intelligence, Medical Diagnosis, Grad-CAM

DOI: 10.54941/ahfe1005960