Summary of Ai-powered Intracranial Hemorrhage Detection: a Co-scale Convolutional Attention Model with Uncertainty-based Fuzzy Integral Operator and Feature Screening, by Mehdi Hosseini Chagahi et al.
AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening
by Mehdi Hosseini Chagahi, Md. Jalil Piran, Niloufar Delfan, Behzad Moshiri, Jaber Hatam Parikhan
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a novel approach for detecting intracranial hemorrhage (ICH) and subdural hemorrhage (SDH) using computed tomography (CT) scan images. The authors propose a two-layered CCA classifier architecture to classify ICH occurrence/non-occurrence and SDH type. In the first layer, features are extracted from CT slices, and the most informative 50 components are selected based on variance and discriminative power. A boosting neural network is then used as a latent feature space. The second layer employs an uncertainty-based fuzzy integral operator to fuse information from different CT slices, improving detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help doctors quickly diagnose a serious brain condition called intracranial hemorrhage. This condition happens when blood vessels in the brain burst and cause bleeding inside or around the skull. If left untreated, it can lead to serious problems like coma or even death. The researchers developed a new way to use CT scan images to detect if someone has this condition and what type of bleeding is happening. They did this by using special computer algorithms that help doctors understand why they are making certain predictions. |
Keywords
» Artificial intelligence » Boosting » Neural network
