yilmaz0734/BrainCTImageStrokeDetection-Segmentation
This project firstly aims to classify brain CT images using convolutional neural networks. In the second stage, the task is segmentation with Unet.
This project helps medical professionals quickly analyze brain CT scans to identify if a stroke is present and, if so, precisely locate the affected area. You input raw brain CT images, and the system outputs a classification of 'Stroke' or 'Non-Stroke' along with a highlighted image showing the exact region of the stroke. This tool is designed for radiologists, neurologists, and emergency room physicians.
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Use this if you need a rapid, automated assessment of brain CT scans to detect and segment strokes, aiding in quicker diagnosis and treatment planning.
Not ideal if you require a system that differentiates between various stroke types beyond hemorrhagic and ischemic, or if you need to analyze image modalities other than CT scans.
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26
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6
Language
Jupyter Notebook
License
MIT
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Last pushed
Jul 30, 2022
Commits (30d)
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