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a/README.md |
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<h2 align="center"> Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis<h2> |
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<h2 align="center"> Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis<h2> |
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## Dataset |
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## Dataset |
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The [```White-Blood-Cell-Detection-Dataset```](https://github.com/medmabcf/White-Blood-Cell-Detection-Dataset) has been used for automatic identification and counting of white blood cell types. (It has been modified and augmented) . Download the dataset, unzip and put |
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The [```White-Blood-Cell-Detection-Dataset```](https://github.com/medmabcf/White-Blood-Cell-Detection-Dataset) has been used for automatic identification and counting of white blood cell types. (It has been modified and augmented) . Download the dataset, unzip and put
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the ```Training```, ```Testing```, and ```Validation```folders in the working directory. |
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the ```Training```, ```Testing```, and ```Validation```folders in the working directory. |
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## Requirements |
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## Requirements |
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Follow these steps to set up your environment: |
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Follow these steps to set up your environment: |
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1. **Clone the YOLOv7 model:** |
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1. **Clone the YOLOv7 model:**
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```shell |
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```shell
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Remove-Item -Path models\yolov7 -Recurse -Force |
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Remove-Item -Path models\yolov7 -Recurse -Force
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git clone https://github.com/WongKinYiu/yolov7.git models\yolov7 |
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git clone https://github.com/WongKinYiu/yolov7.git models\yolov7
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``` |
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``` |
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2. **Clone the Segment Anything model:** |
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2. **Clone the Segment Anything model:**
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```shell |
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```shell
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Remove-Item -Path models\segment-anything -Recurse -Force |
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Remove-Item -Path models\segment-anything -Recurse -Force
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git clone https://github.com/facebookresearch/segment-anything.git models\segment-anything |
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git clone https://github.com/facebookresearch/segment-anything.git models\segment-anything
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``` |
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``` |
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3. **Install the required Python packages:** |
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3. **Install the required Python packages:**
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```shell |
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```shell
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pip install -r requirements.txt |
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pip install -r requirements.txt
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``` |
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``` |
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After you've classified the white blood cells, if you want to extract lymphocyte cells and check if the cell is normal or abnormal (acute leukemia blast), you should download the Segment Anything Model (SAM) weights by running the following command: |
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After you've classified the white blood cells, if you want to extract lymphocyte cells and check if the cell is normal or abnormal (acute leukemia blast), you should download the Segment Anything Model (SAM) weights by running the following command: |
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```shell |
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```shell
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python Download_sam_weights |
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python Download_sam_weights
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``` |
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```
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## Getting Started |
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## Getting Started |
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1. Run detectcell.py |
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1. Run detectcell.py
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```python detectcell.py``` |
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```python detectcell.py``` |
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## How to Run the Code :runner: |
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## How to Run the Code :runner: |
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## Blood Cell Detection Output |
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## Blood Cell Detection Output |
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<p align="center"> |
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<p align="center">
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im022_1/Im022_1.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im022_1/Im022_1.jpg?raw=true" width="500">
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im043_0/Im043_0.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im043_0/Im043_0.jpg?raw=true" width="500">
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im080_0/Im080_0.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im080_0/Im080_0.jpg?raw=true" width="500">
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im079_0/Im079_0.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im079_0/Im079_0.jpg?raw=true" width="500">
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im076_0/Im076_0.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/output/Im076_0/Im076_0.jpg?raw=true" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/outred/Im076_0/Im076_0.jpg" width="500"> |
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<img src="https://github.com/medmabcf/Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis/blob/main/cell_application/outred/Im076_0/Im076_0.jpg?raw=true" width="500">
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</p> |
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</p> |
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