An Effective Deep Learning Based Model for the Prediction of Osteoporosis from Knee X-Ray Images
Keywords:
Osteoporosis, Deep learning, ResNet 50, Knee X-ray images, Gated Current Unit (GRU)Abstract
Osteoporosis is a disease, that makes the bone brittle and weak which occurs mainly in elderly and in women who have gone through menopause. More affordable diagnosing systems are needed as the high costs of diagnosis and treatment make them unaffordable. This research introduces a novel deep learning (DL) based model for the early prediction of osteoporosis from knee X-ray images, addressing the critical need for timely diagnosis of this bone disease. Utilizing a ResNet50-GRU hybrid architecture, the model effectively captures both spatial and temporal relationships within the X-ray data, obtaining remarkable results with 95.65% precision, 95.32% accuracy, 95.98% recall, and 95.49% F1-score. The suggested model demonstrates robust performance in classifying osteoporosis and normal cases. Through extensive evaluation on a dataset of knee X-ray images, this research contributes a powerful tool for healthcare professionals to enhance early osteoporosis detection, potentially improving patient outcomes and reducing associated healthcare costs.
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