Ensemble Classifier for Enhancing Osteoporosis Diagnosis Accuracy

The classification of healthy and diseased bone images poses a significant challenge due to their high degree of similarity, which often makes it difficult for conventional methods to accurately distinguish between them. This challenge is particularly pronounced in diseases like osteoporosis, where subtle changes in bone texture can indicate the presence of the condition. Therefore, the development of an effective technique for characterizing bone texture becomes paramount in diagnosing such diseases with precision and reliability. In this paper, we propose an innovative automated osteoporosis detection scheme to address this pressing need. Our approach advantages advanced feature extraction methods to thoroughly analyze bone images and capture nuanced patterns indicative of osteoporosis or other bone-related diseases. By employing a sophisticated classifier known as Ensemble Classifiers (EC), our method aims to improve the accuracy and robustness of osteoporosis detection. Through rigorous validation using a comprehensive test set, we demonstrate the remarkable performance of our scheme, with the Ensemble Classifiers achieving an impressive accuracy of 98.59%. These results signify a significant advancement over traditional methods, including the commonly used SVM classifier, highlighting the effectiveness and potential clinical utility of our proposed approach in enhancing osteoporosis diagnosis and patient care.