Summary of Spatial Sequence Attention Network For Schizophrenia Classification From Structural Brain Mr Images, by Nagur Shareef Shaik and Teja Krishna Cherukuri and Vince Calhoun and Dong Hye Ye
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
by Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince Calhoun, Dong Hye Ye
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 machine learning-based approach is introduced for classifying individuals with schizophrenia, leveraging deep learning and spatial sequence attention (SSA) to extract significant feature representations from structural MRI (sMRI). The proposed method employs transfer learning using pre-trained DenseNet to extract initial features, which are then processed by SSA to capture spatial interactions and relationships within the brain. Experimental results on a clinical dataset show that SSA outperforms Squeeze & Excitation Network for schizophrenia classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Schizophrenia is a serious mental disorder that affects people’s thinking, behavior, and social skills. The problem is that it can be hard to diagnose just by looking at someone’s brain. This study uses special computer algorithms to help doctors detect Schizophrenia more accurately. They use something called “deep learning” and “attention mechanism” to analyze MRI scans of the brain. The results show that this method works better than others for diagnosing Schizophrenia. |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning » Machine learning » Transfer learning
