International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Elderly Knee and Leg Pain Diagnosis System |
|---|---|
| ABSTRACT | Knee and leg pain can limit mobility, independence, and quality of life among older adults. This paper presents the Elderly Knee and Leg Pain Diagnosis System, a web-based prototype intended to organize user-reported symptoms and provide preliminary decision-support information. The system combines a weighted rule-based symptom assessment with a deep-learning module that estimates radiographic knee osteoarthritis severity using the Kellgren–Lawrence (KL) grading scale. The symptom module considers inputs such as pain severity, onset, swelling, fever, ability to bear weight, crepitus, locking, numbness, weakness, injury history, morning stiffness, age, and selected medical history. Safety rules identify selected symptom combinations that warrant urgent medical assessment. For X-ray analysis, the system preprocesses uploaded images to 224 × 224 pixels and applies a MobileNetV2-based model with an ordinal output design to estimate KL grades 0–4. The application includes a web interface, FastAPI backend, downloadable PDF assessment reports, and a recent-assessment history feature. In the stored test evaluation of 1,656 images, exact-grade accuracy was 47.83%, while accuracy within one grade was 72.71%. Class-wise recall was uneven, including 0% recall for grade 1, indicating that the model requires substantial improvement and further validation. The prototype is therefore intended for educational and preliminary decision support only, not for independent clinical diagnosis. Further work includes model refinement, external validation, clinician review, accessibility testing, and privacy-focused deployment. |
| AUTHOR | PROF. USHA K, MALA P, MOHAMMED MALIK REHAN DARWESH, MOHAMMED MAQSOOD ALI, MOHAMMED YASEER Assistant Professor, Dept. of Computer Science and Engineering, Jain Institute of Technology, Davanagere, Karnataka, India UG Students, Department of Computer Science and Engineering, Jain Institute of Technology, Davanagere, Karnataka, India |
| VOLUME | 190 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1411007 |
| pdf/7_Elderly Knee and Leg Pain Diagnosis System.pdf | |
| KEYWORDS | |
| References |
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