Summary of Enhancing Osteoporosis Detection: An Explainable Multi-modal Learning Framework with Feature Fusion and Variable Clustering, by Mehdi Hosseini Chagahi et al.
Enhancing Osteoporosis Detection: An Explainable Multi-Modal Learning Framework with Feature Fusion and Variable Clustering
by Mehdi Hosseini Chagahi, Saeed Mohammadi Dashtaki, Niloufar Delfan, Nadia Mohammadi, Alireza Samari, Behzad Moshiri, Md. Jalil Piran, Oliver Faust
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-modal learning framework is proposed to improve diagnostic accuracy for osteoporosis using both clinical and imaging data. The approach integrates three pre-trained networks (VGG19, InceptionV3, and ResNet50) to extract deep features from X-ray images, which are then transformed using PCA to reduce dimensionality. A clustering-based selection process identifies the most representative components, combined with preprocessed clinical data and processed through a fully connected network (FCN) for final classification. The feature importance plot highlights key variables, showing that Medical History, BMI, and Height were the main contributors, emphasizing the significance of patient-specific data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors diagnose osteoporosis better by combining X-ray images with other health information. It uses special computers (AI) to find patterns in the images and health data. The results show that the most important factors for predicting osteoporosis are a person’s medical history, weight, and height. While the imaging features are helpful, they’re not as important as the clinical data. This approach can make AI-driven diagnoses more accurate and trustworthy. |
Keywords
» Artificial intelligence » Classification » Clustering » Multi modal » Pca
