Leveraging Advanced Deep Learning Architectures for Precise Knee Osteoporosis Prediction: A Paradigm Shift in Diagnostic Healthcare

Osteoporosis is a condition characterized by reduced bone mass and deterioration of bone tissue. It affects the people by increasing bone fragility and a higher risk of fractures. Globally, it affects approximately 200 million people, predominantly postmenopausal women and the elderly. Early detection of osteoporosis is crucial for reducing fracture risks, improving patient outcomes, and lowering healthcare costs. However, traditional diagnostic methods are often invasive and costly, particularly in developing regions. Current methods lack efficiency and affordability, underscoring the need for noninvasive and cost-effective diagnostic solutions. This paper explores the use of various deep learning techniques such as CNN, VGG16, ResNet, DenseNet, AlexNet, and GoogLeNet. Using deep learning models on knee X-ray images, this effort provides a simple, non-invasive approach to early osteoporosis detection. The models are assessed using accuracy metrics, and cross-validation on training, validation, and test datasets is used to validate their performance. The models’ classification performance is also evaluated using F1-scores, accuracy, recall, and confusion matrices. The models are employed to analyze the images, with the aim of improving diagnostic accuracy and enabling early intervention. The overall efficiency is improved by using data augmentation approaches, optimizing the models and implementing efficient preprocessing steps. This study’s primary objective is to develop and evaluate deep learning models that can use knee X-ray images to identify osteoporosis early, offering a quicker, non-invasive, and easier method of diagnosis.