Fuzzy Rank-Based Ensemble Model for Accurate Diagnosis of Osteoporosis in Knee Radiographs

The main factor in fractures among seniors and women post-menopausal is osteoporosis, which decreases the density of bones. Finding a low-cost diagnostic technology to identify osteoporosis in its initial stages is imperative considering the substantial expenses of diagnosis and therapy. The simplest and most widely used imaging method for detecting bone diseases is X-ray radiography, however, it is problematic to manually examine X-rays for osteoporosis as well as to identify the essential components and choose elevated classifiers. To categorize x-ray pictures of knee joints into normal, osteopenia, and osteoporosis condition categories, authors present a process in this investigation that uses three convolutional neural networks (CNN) architectures, i.e., Inception v3, Xception, and ResNet 18, to create an ensemble-based classifier model. The suggested ensemble approach employs a fuzzy rank-based unification of classifiers by taking into account two distinct parameters on the decision scores produced by the aforementioned base classifiers. Contrary to the straightforward fusion strategies that have been mentioned in the literature, the suggested ensemble methodology finalizes predictions on the test specimens by considering the confidence in the recommendations of the base learners. A 5-fold cross-validation approach has been employed to assess the developed framework using a benchmark dataset that has been made accessible to the general population. The suggested model yields an accuracy rate of 93.5% with a loss of 0.082. Further, the AUC is observed to be 98.1, 97.9 and 97.3 for normal, osteopenia and osteoporosis, respectively. The results demonstrate the model's usefulness by outperforming various state-of-the-art approaches.