Automated-Leukemia-Detection-using-YOLO-based-Blood-Cell-Analysis

## Dataset 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 the ```Training```, ```Testing```, and ```Validation```folders in the working directory. ## Requirements Follow these steps to set up your environment: 1. **Clone the YOLOv7 model:** ```shell Remove-Item -Path models\yolov7 -Recurse -Force git clone https://github.com/WongKinYiu/yolov7.git models\yolov7 ``` 2. **Clone the Segment Anything model:** ```shell Remove-Item -Path models\segment-anything -Recurse -Force git clone https://github.com/facebookresearch/segment-anything.git models\segment-anything ``` 3. **Install the required Python packages:** ```shell pip install -r requirements.txt ``` 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:

python Download_sam_weights

## Getting Started 1. Run detectcell.py ```python detectcell.py``` ## How to Run the Code :runner: ## Blood Cell Detection Output