Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach

Abstract

Bone deterioration from osteoporosis creates fractures that primarily affect females who have reached menopause and older adults. Early detection of osteoporosis requires affordable methods because current diagnostic systems are both expensive and challenging to use. The current application of deep learning models for bone radiography analysis faces three major limitations: shallow architecture, insufficient dataset adaptation, and inadequate feature extraction methods. Talk-based machine learning techniques rely heavily on human-generated feature construction; however, they fail to recognize the delicate image patterns that are present in medical contexts. VGG-16 provides strong power, but businesses must invest in advanced and expensive hardware platforms while maintaining challenges with data quantity. The detection of osteoporosis in knee X-rays through deep learning and the ResNet-50 model using transfer learning remains our primary proposal for solving existing issues. An overview of 372 X-ray images with verified medical information was constructed for T-score ratings determined using a quantitative ultrasound system. Following fine-tuning of our model based on ResNet-50, we achieved 90% accuracy, which exceeded all alternative models tested, including VGG-16 with 88% accuracy using fine-tuning, 80% without fine-tuning, a 3-layer CNN at 66%, ResNet-18 at 79%, and non-fine-tuned ResNet-50 at 83%. The clinical value of the ResNet-50 model stems from its high accuracy, strong sensitivity, and specificity rates. The detection reliability of osteoporosis by the deep transfer learning CNN ResNet-50 has been proven through these findings, which provide healthcare practitioners with an effective diagnostic tool for early detection and prevention of fractures. Our research indicates that deep learning using transfer learning strategies produces a crucial enhancement in medical imaging systems, leading to better patient results.

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