Computer-Aided System for the Detection of Rheumatoid Arthritis
Keywords:
Rheumatoid arthritis, Deep learning, Disease detection, Medical ImagingAbstract
Rheumatoid Arthritis (RA) is a chronic disease that causes disability in movement. RA classification is critical for effective diagnosis and treatment planning. This work explores the application of the EfficientNetB6 architecture using transfer learning to classify RA severity into three categories: Healthy, Moderate and Severe. Medical imaging dataset containing X-Ray images, enhanced with contrast limited adaptive histogram equalization (CLAHE), data augmentation techniques and fine-tuning of hyper-parameters were applied in this work. We compared EfficientNetB6 with all the models of EfficientNet family and all other state of the art models. When we combined EfficientNetB6 with CLAHE, we achieved the highest accuracy of 96.06%. Without using CLAHE accuracy dropped by 4% to 5% for all the models. For healthy class model, we achieved precision, recall and F1-score of 99.36%,97.81%,98.58% respectively, showing robustness in identifying healthy cases. Moderate class yielded precision, recall and F1-score of 89.45%,95.07%,92.17% respectively, demonstrating the model’s effectiveness in identifying moderate cases with minimal false negatives. The Severe class presented more challenges with a precision, recall and F1-score of 85.11%,78.43%, 81.63% highlighting the need for improved recall value. To further improve results we suggest enhancements such as advanced data augmentation and synthetic data generation, particularly for the Severe class consequently aiding clinicians for identification of RA.
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