对比CNN与FNN在骨质疏松检测中的表现，发现FNN略胜一筹。
Analysing Osteoporosis Detection: A Comparative Study of CNN and FNN
- 用CNN和FNN分析DEXA影像，比较其检测效率
- FNN准确率达95%，略高于CNN的93%
- 适合医学影像分析与AI辅助诊断研究者参考
骨质疏松导致骨密度和强度渐进性下降，使骨折风险显著高于健康骨骼。据估计，全球50岁以上人群中约1/3女性和1/5男性将经历骨质疏松性骨折，已成为重大公共卫生问题。诊断基础为骨密度（BMD）检测，双能X射线吸收测定法（DEXA）最为常用，T-score ≤ -2.5 定义为骨质疏松。本文聚焦于医学影像分析在骨质疏松检测中的应用，通过对比卷积神经网络（CNN）与前馈神经网络（FNN）在DEXA图像分析中的表现。两种模型均表现良好，其中FNN准确率达到95%，略高于CNN的93%。研究表明，深度学习技术有望提升骨质疏松检测能力，优化诊断工具，改善患者预后。
原文摘要 · Abstract (English)
Osteoporosis causes progressive loss of bone density and strength, causing a more elevated risk of fracture than in normal healthy bones. It is estimated that some 1 in 3 women and 1 in 5 men over the age of 50 will experience osteoporotic fractures, which poses osteoporosis as an important public health problem worldwide. The basis of diagnosis is based on Bone Mineral Density (BMD) tests, with Dual-energy X-ray Absorptiometry (DEXA) being the most common. A T-score of -2.5 or lower defines osteoporosis. This paper focuses on the application of medical imaging analytics towards the detection of osteoporosis by conducting a comparative study of the efficiency of CNN and FNN in DEXA image analytics. Both models are very promising, although, at 95%, the FNN marginally outperformed the CNN at 93%. Hence, this research underlines the probable capability of deep learning techniques in improving the detection of osteoporosis and optimizing diagnostic tools in order to achieve better patient outcomes.
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