Automatic Identification and Counting of Blood Cells

[](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/stargazers) [](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/network) [](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/issues) [](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells) [](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/blob/master/LICENSE) ## Dataset The [```Complete Blood Count (CBC) Dataset```](https://github.com/MahmudulAlam/Complete-Blood-Cell-Count-Dataset) has been used for automatic identification and counting of blood cells. Download the dataset, unzip and put the ```Training```, ```Testing```, and ```Validation```folders in the working directory. ## Requirements   - Tensorflow-GPU==2.2.0 (tested on 2.1.0, 2.2.0, and 2.3.0) ```conda install tensorflow-gpu``` - TF-slim==1.1.0 ```pip install tf-slim==1.1.0``` - Weights: [```download```](https://1drv.ms/u/s!AlXVRhh1rUKThlxTievX0X1CpXd0?e=9cKxYb) the trained weights file for blood cell detection and put the ```weights``` folder in the working directory. [](https://1drv.ms/u/s!AlXVRhh1rUKThlxTievX0X1CpXd0?e=9cKxYb) [](https://mega.nz/#F!2kVUnKjS!z15tM9WLfga3l1gCNSLNGw) ## Getting Started 1. Build the cython extension in place ```python setup.py build_ext --inplace``` 2. Run detect.py ```python detect.py``` ## Update The ```darkflow.cython_utils.cy_yolo_findboxes``` problem has been fixed. Make sure to build the cython extension in place before running the code. [](https://www.tensorflow.org/install) The code was originally written and developed with `TensorFlow v1.x`. The new updated version `v2.0` included `TensorFlow v2.x` support, tested on both TensorFlow `v2.1.0` and `v2.2.0`. You can download the previous version from **[`here`](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/releases/tag/v1.0)** . ## How to Run the Code :runner: To detect the blood cells, simply run the `detect.py` file in the terminal or use an IDE. A step-by-step guideline of how to run the blood cell detection code in your computer is provided in this **[`wiki`](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/wiki/A-Step-by-Step-Guide-of-How-to-Run-the-Code)** . If you have any trouble running the code and facing any errors please feel free to create an **[`issue`](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/issues)** or **[`contact me`](https://mahmudulalam.github.io/#contact)**. ## How to Train on Your Dataset :bullettrain_side: A seven-step guideline of how to train on your own dataset is provided in this **[`wiki`](https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-Cells/wiki/How-to-Train-on-Your-Dataset)** . ## Paper [](http://ietdl.org/t/kmgztb) [](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5098) The code was developed for the following blood cell detection paper. For a more detailed explanation of the proposed method, please go through the pdf of the [```paper```][1]. If you use this code or associated dataset, please cite this paper as: [***```Machine learning approach of automatic identification and counting of blood cells```***](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5098) ```bibtex @article{alam2019machine, title={Machine learning approach of automatic identification and counting of blood cells}, author={Alam, Mohammad Mahmudul and Islam, Mohammad Tariqul}, journal={Healthcare Technology Letters}, volume={6}, number={4}, pages={103--108}, year={2019}, publisher={IET} } ``` ## Blood Cell Detection Output

## KNN and IOU Based Verification In some cases, our model predicts the same platelet twice. To solve this problem we propose a k-nearest neighbor (KNN) and intersection over union (IOU) based verification system where we find the nearest platelet of a selected platelet and calculate their overlap. We are allowing only a 10% overlap between two platelets. If the overlap is more than that then it will be a spurious prediction and we will ignore the prediction. | Before Verification | After Verification | |:-:|:-:| |

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| ## Prediction on High-Resolution Image (HRI) We have used our model to detect and count blood cells from high-resolution blood cell smear images. These test images are of the size of ```3872 x 2592``` way higher than the size of our trained images of ```640 x 480```. So, to match the cell size of our trained images we divide those images into grid cells and run prediction in each grid cell and then combine all the prediction results.

Dividing Image into Grid/Patch

Combined Output