Abstract
This work uses T1, STIR, and T2 MRI sequences of the lumbar vertebrae and BMD measurements to identify osteoporosis using deep learning. An analysis of 1350 MRI images from 50 individuals who had simultaneous BMD and MRI scans was performed. The accuracy of a custom convolution neural network for osteoporosis categorization was assessed using deep learning. T2-weighted MRIs were most diagnostic. The suggested model outperformed T1 and STIR sequences with 88.5% accuracy, 88.9% sensitivity, and 76.1% F1-score. Modern deep learning models like GoogleNet, EfficientNet-B3, ResNet50, InceptionV3, and InceptionResNetV2 were compared to its performance. These designs performed well, but our model was more sensitive and accurate. This research shows that T2-weighted MRI is the best sequence for osteoporosis diagnosis and that deep learning overcomes BMD-based approaches by reducing ionizing radiation. These results support clinical use of deep learning with MRI for safe, accurate, and quick osteoporosis diagnosis.