# Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules

arXiv: https://arxiv.org/abs/2412.09330 · HTML: https://mlsanity.com/paper/2412.09330/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 53 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Machine Learning-Based Differential Diagnosis of Parkinson's Disease Using Kinematic Feature Extraction and Selection](https://mlsanity.com/paper/2501.02014/) (2025)

## Abstract (arXiv)

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.