Knee osteoporosis weakens the bone tissue in the knee joint, increasing fracture risk. Early detection through X-ray images enables timely intervention and improved patient outcomes. While some researchers have focused on diagnosing knee osteoporosis through manual radiology evaluation and traditional machine learning using hand-crafted features, these methods often struggle with performance and efficiency due to reliance on manual feature extraction and subjective interpretation. In this study, we propose a computer-aided diagnosis (CAD) system for knee osteoporosis, combining transfer learning with stacked feature enhancement deep learning blocks. Initially, knee X-ray images are preprocessed, and features are extracted using a pre-trained Convolutional Neural Network (CNN). These features are then enhanced through five sequential Conv-RELU-MaxPooling blocks. The Conv2D layers detect low-level features, while the ReLU activations introduce non-linearity, allowing the network to learn complex patterns. MaxPooling layers down-sample the features, retaining the most important spatial information. This sequential processing enables the model to capture complex, high-level features related to bone structure, joint deformation, and osteoporotic markers. The enhanced features are passed through a classification module to differentiate between healthy and osteoporotic knee conditions. Extensive experiments on three individual datasets and a combined dataset demonstrate that our model achieves 97.32%, 98.24%, 97.27%, and 98.00% accuracy for OKX Kaggle Binary, KXO-Mendeley Multi-Class, OKX Kaggle Multi-Class, and the combined dataset, respectively, showing an improvement of around 2% over existing methods.

翻译：膝关节骨质疏松症会削弱膝关节骨组织，增加骨折风险。通过X射线图像进行早期检测可实现及时干预并改善患者预后。尽管已有研究者通过手动放射学评估及使用手工特征的传统机器学习方法进行膝关节骨质疏松诊断，但这些方法因依赖人工特征提取和主观解释，通常在性能与效率方面存在局限。本研究提出一种结合迁移学习与堆叠特征增强深度学习模块的计算机辅助诊断系统。首先对膝关节X射线图像进行预处理，并利用预训练的卷积神经网络提取特征。随后通过五个连续的Conv-RELU-MaxPooling模块增强特征：Conv2D层检测低级特征，ReLU激活函数引入非线性使网络能学习复杂模式，MaxPooling层对特征进行下采样并保留关键空间信息。这种序列处理使模型能够捕捉与骨结构、关节变形及骨质疏松标志相关的复杂高级特征。增强后的特征通过分类模块区分健康与骨质疏松膝关节状态。在三个独立数据集及组合数据集上的实验表明，本模型在OKX Kaggle二分类、KXO-Mendeley多分类、OKX Kaggle多分类及组合数据集上分别达到97.32%、98.24%、97.27%和98.00%的准确率，较现有方法提升约2%。