OSIS-NET Deep Learning Framework for Enhanced Osteoporosis Detection and Classification Using MRI Imaging

Osteoporosis is a major public health issue due to its association with increased fracture risk and reduced quality of life. Early diagnosis is crucial for effective treatment and prevention. This study introduces OSIS-NET, an advanced deep learning-based framework for detecting and classifying osteoporosis using MRI imaging. The model integrates convolutional neural networks (CNNs) with machine learning techniques to enhance accuracy, computational efficiency, and clinical reliability. The next stage applies deep learning (DL) based Dilated leaky ShuffleNet to process images for extracting textural and shape features from them. The selected ST features undergo an evaluation process using ML models which includes SVM, KNN, DT, and RF for detecting normal and abnormal patterns. The system reaches an excellent accuracy level that exceeds 99.76 % for normal cases and 99.89% for abnormal cases. The model achieves 0.86% superior accuracy when compared to GoogleNet alongside 0.54% and 0.16 % and 1.07 % superior results than ShuffleNet, AlexNet and MobileNet respectively. The study addresses model complexity, validation methodologies, and performance improvement strategies. Compared to traditional approaches, OSIS-NET demonstrates superior classification accuracy. We discuss the framework's real-world applications, limitations, and future directions for optimizing its robustness and clinical deployment.