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# GI Bleeding Detection



A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.

## Features

- **Automated Bleeding Detection**: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions
...
- **Quantitative Analysis**: Calculates bleeding area percentage and provides risk assessment
- **Report Generation**: Creates saveable reports for medical records and further analysis

## Screenshots

















## Installation

### Prerequisites

...
- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation
- [OpenCV](https://opencv.org/) library for computer vision algorithms
- [scikit-learn](https://scikit-learn.org/) for machine learning components

	a/README.md		b/README.md
1	# GI Bleeding Detection	1	# GI Bleeding Detection
2		2
3	![GI Bleeding Detection Banner](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/banner.png?raw=true)
4
5	A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.	3	A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.
6		4
7	## Features	5	## Features
8		6
9	- Automated Bleeding Detection: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions	7	- Automated Bleeding Detection: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions
	...		...
12	- Quantitative Analysis: Calculates bleeding area percentage and provides risk assessment	10	- Quantitative Analysis: Calculates bleeding area percentage and provides risk assessment
13	- Report Generation: Creates saveable reports for medical records and further analysis	11	- Report Generation: Creates saveable reports for medical records and further analysis
14		12
15	## Screenshots	13	## Screenshots
16		14
17	### Main Application Interface	15
18		16
19	![Main Interface](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/main-interface.png?raw=true)
20
21	### Analysis Results Example
22
23	![Analysis Results](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/analysis-results.png?raw=true)
24
25	### Bleeding Detection Visualization
26
27	Original Endoscopic Image
28	![Original Image](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/original-image.png)
29
30	Bleeding Detection Result
31	![Bleeding Detection](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/bleeding-detection.png?raw=true)
32		17
33	## Installation	18	## Installation
34		19
35	### Prerequisites	20	### Prerequisites
36		21
	...		...
204	- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation	189	- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation
205	- [OpenCV](https://opencv.org/) library for computer vision algorithms	190	- [OpenCV](https://opencv.org/) library for computer vision algorithms
206	- [scikit-learn](https://scikit-learn.org/) for machine learning components	191	- [scikit-learn](https://scikit-learn.org/) for machine learning components