An Ensemble Methodology for Osteoporosis Prediction Using Knee X-Ray Images

Abstract

Osteoporosis is the most prominent chronic bone disorder characterized by a deficit bone mineral density (BMD), which elevates the probability of bone fractures. An early and precise osteoporosis diagnosis increases the patient's survival rate. X-ray imaging is the most affordable and accessible method for diagnosing bone diseases. Still, manually interpreting X-rays for osteoporosis is laborious and time-consuming, and choosing high-performance classifiers is a highly challenging task. To address the above issues, this paper presents an ensemble-based model that uses knee X-ray images to predict osteoporosis as a binary class (normal and osteoporosis). The model used different transfer learning techniques and a custom CNN to detect osteoporosis in this work. It is found that the ensemble model has produced exceptional outcomes, particularly in terms of accuracy for binary classification. Additionally, the model's efficiency was tested and compared with other deep learning models, which indicated that the ensemble model is more robust than recent DL approaches. Hence, our methodology may save surgeons time while simultaneously enhancing patient outcomes.