article · International Journal of Computational Intelligence Systems
Osteoporosis often progresses without symptoms until a bone fracture happens, making early detection critical yet difficult because conventional diagnostic techniques take considerable time. Deep learning offers an automated route to analyse medical imagery. A dual strategy method uses convolutional neural networks and transfer learning alongside data augmentation to diagnose bone density conditions from knee X-ray imagery. Evaluated on a collection of 1,947 knee radiographs, the models perform both binary detection and three-way classification across normal, osteopenia, and osteoporosis categories. The system also extracts visual feature maps designed to assist clinicians in their diagnostic assessments. Across multiple tested network architectures, an adapted VGG-19 model recorded the strongest performance, reaching 97.5 percent accuracy in binary classification and 92.0 percent accuracy in multiclass evaluation.
Osteoporosis typically remains hidden until painful and debilitating fractures happen, while standard diagnostic paths are often slow. Applying automated computer vision to common knee X-rays enables faster, more accessible screening for early bone loss. This can assist medical teams in identifying risks before severe damage occurs, helping preserve patient mobility and easing the diagnostic workload on clinical staff.
The technology could form the basis of clinical decision-support software for radiologists and orthopaedic clinicians assessing routine knee radiographs. Such software could help identify early bone thinning before fractures develop. Currently at the applied and tested research stage on a dataset of 1,947 images, commercial deployment would require prospective clinical trials, regulatory approvals for medical software, and integration with hospital picture archiving and communication systems.
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Osteoporosis, a silent yet debilitating disease, presents a significant challenge due to its asymptomatic nature until fractures occur. Rapid bone loss outpaces regeneration, leading to pain, disability, and loss of independence. Early detection is pivotal for effective management and fracture risk reduction, yet current diagnostic methods are time-consuming. Despite its importance, research addressing early diagnosis remains limited. Deep learning, particularly convolutional neural networks (CNNs), has emerged as a potent tool in image analysis. This paper presents a novel approach utilizing transfer learning with CNNs for osteoporosis detection from X-ray images. The proposed approach not only achieves a high accuracy of osteoporosis diagnosis but also offers a revealed feature map that can guide medical professionals for osteoporosis diagnosis. The innovation lies in a dual strategy: (i) a model integrating transfer learning from CNN architectures such as AlexNet, VGG-16, ResNet-50, VGG-19, InceptionNet, XceptionNet, and a custom CNN, and (ii) a dataset collection augmentation mechanism to enhance learning accuracy. The study includes binary and multiclass classification of knee joint X-ray images into normal, osteopenia, and osteoporosis groups, utilizing a dataset of 1947 knee X-rays for training and testing. Performance comparisons against state-of-the-art models reveal the proposed VGG-19 model achieves the highest accuracy at 92.0% for multiclass and 97.5% for binary. These findings underscore the potential of deep learning with transfer learning in aiding early osteoporosis detection, thereby mitigating fracture risks.
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DOI: 10.1007/s44196-024-00615-4
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