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@article{174146,
        author = {Aditya R. Chopkar and Prof. M.M. Baig and Shubham Padole and Anushka kesharwani and Om Dhote},
        title = {Optimizing Rheumatoid Arthritis Detection through Advanced Pre-Processing using Deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2788-2792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174146},
        abstract = {Abstract for the "Optimizing Rheumatoid Arthritis Detection through Advanced Pre-Processing and Deep Learning" topic may be written as below: Rheumatoid arthritis (RA) is a long-standing autoimmune disease needing early diagnosis to manage it successfully. This work investigates RA detection optimization with sophisticated pre-processing strategies and convolutional neural networks (CNNs) in association with deep learning algorithms. The work strives to make RA diagnosis more accurate and efficient with new image analysis techniques. The work follows a multi-stage methodology beginning with data acquisition and pre-processing of X-ray and other imaging modalities. Advanced pre-processing techniques are applied for the extraction of relevant features and enhancement of image quality. The pre-processed dataset is then utilized to design and train a CNN architecture specially meant for RA identification. These measurements such as sensitivity, specificity, accuracy, and area under ROC curve are applied to measure the performance of the model. There are significant improvements in RA accuracy detection compared to traditional methods from the results. The improved capability of the CNN model to recognize RA in early stages and distinguish it from other joint diseases is proven. The ability of the model to monitor the progression of a disease and generate personalized treatment plans is also discussed in the study. This work contributes to medical image analysis by presenting the capability of integrating sophisticated pre-processing with deep learning for better RA detection. The results hold implications for treatment optimization, early diagnosis, and patient care overall in rheumatology.},
        keywords = {Rheumatoid Arthritis (RA), Autoimmune Disease, Convolutional Neural},
        month = {March},
        }