Deep ensemble learning for osteoporosis diagnosis from knee X-rays: a preliminary cohort study in Kashmir valley

Abstract

Osteoporosis (OP) is the most prevalent and common bone disease, especially knee osteoporosis. It significantly disables sufferers all over the world. Although laborious and prone to user variation, manual diagnosis, segmentation, and annotation of knee joints continue to be the preferred way to diagnose OP in clinical procedures. Therefore, many deep learning algorithms, particularly the convolutional neural network (CNN), have been created to increase clinical workflow efficiency to overcome the shortcomings of the widely used method as above. Medical imaging procedures can show hidden structures in a volumetric view, particularly those that generate three-dimensional (3D) pictures like MRI. We created a dataset of 240 pictures from patients who had knee X-rays and skeletal bone mineral density assessments at the same time. Four convolutional neural networks (CNN) models were used to analyse the X-ray images and deep neural networks for clinical covariances to determine the degree of osteoporosis. Additionally, we investigated ensemble models that included each CNN with a clinical covariance. For every network, scores for accuracy and error rate were computed. ResNet and Alexnet displayed the highest levels of accuracy when the CNN models were tested using knee X-rays with normal, low BMD, and osteoporosis. An ensemble of DNN with Alexnet, ResNet, and both ResNet and Alexnet are employed resulting in improved accuracy. The ensemble of best-performing CNN and DNN is proposed to diagnose osteoporosis more accurately. The proposed method has produced a highly accurate osteoporosis diagnosis.