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https://github.com/fahriwps/breast-cancer-detection
Breast cancer mass detection using YOLO object detection algorithm and GUI
https://github.com/fahriwps/breast-cancer-detection
cancer deep-learning opencv python yolov3
Last synced: 12 days ago
JSON representation
Breast cancer mass detection using YOLO object detection algorithm and GUI
- Host: GitHub
- URL: https://github.com/fahriwps/breast-cancer-detection
- Owner: fahriwps
- License: gpl-3.0
- Created: 2020-08-10T17:16:28.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-04T02:30:22.000Z (about 1 year ago)
- Last Synced: 2024-02-29T10:33:50.394Z (4 months ago)
- Topics: cancer, deep-learning, opencv, python, yolov3
- Language: Python
- Homepage:
- Size: 350 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Lists
- awesome-yolo-object-detection - fahriwps/breast-cancer-detection - cancer-detection?style=social"/> : Breast cancer mass detection using YOLO object detection algorithm and GUI. (Applications)
README
# Introduction
NeuralSIGHT is our final bachelor capstone project for detecting breast cancer mass using YOLO darknet and PyQt.
## Requirements
- **PyQt5**
- **YOLO darknet installed**
- **OpenCV >= 4.4.0**
## How To Use
#### Step 0: Compile YOLO darknet
Follow AlexeyAB's instruction of [How to compile YOLO darknet on Linux or Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-linuxmacos-using-cmake)
#### Step 1: Install PyQt5 and OpenCV
Simple installation from PyPI
```bash
pip install PyQt5
pip install opencv-python
```
From Conda
```bash
conda install -c anaconda pyqt
```
#### Step 2: Setup Folder
- Move `mainCV.py`,`main_windowCV.py`, and `splashscreen.py` to your darknet folder.
- Put your `your-weight.weights` to weights folder.
#### Step 3: Run Program
Run `mainCV.py` for OpenCV version inference
## NeuralSIGHT Team
* [fahriwps](https://github.com/fahriwps)
* [mariojy10](https://github.com/mariojy10)