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--- a +++ b/README.md @@ -0,0 +1,120 @@ +<h2 align="center">Automatic Identification and Counting of Blood Cells<h2> + +[](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) +<!--<img src="https://user-images.githubusercontent.com/37298971/123714340-f8d70800-d82a-11eb-9742-042a5d9334a1.png" width="28">--> + +## 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 + +<p align="center"> + <img src="https://user-images.githubusercontent.com/37298971/44617785-17eb0980-a88b-11e8-9018-c84f8be5cefa.png" width="500"> +</p> + +## 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 | +|:-:|:-:| +| <p align="center"> <img src="https://user-images.githubusercontent.com/37298971/46260207-b27ede00-c504-11e8-9d00-7d7be151ee5d.jpg"> </p> | <p align="center"> <img src="https://user-images.githubusercontent.com/37298971/46260504-a268fd80-c508-11e8-9ef0-5230d00f47a3.jpg"> | + +## 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. + +<h3 align="center">Dividing Image into Grid/Patch</h3> +<p align="center"> + <img src="https://user-images.githubusercontent.com/37298971/45962420-a39ab600-c042-11e8-975f-9b0a077f0e0f.jpg" width="700"> +</p> + +<h3 align="center">Combined Output</h3> +<p align="center"> + <img src="https://user-images.githubusercontent.com/37298971/45961699-055a2080-c041-11e8-95b0-1c8ac3c8875b.jpg" width="700"> +</p> + +[1]: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl.2018.5098