augustoschnorr/automatic-identification-and-counting-of-blood-cells

A machine learning approach of automatic identification and counting of all types of blood cells: RBCs, WBCs, and Platelets with K-nearest neighbor (KNN) and intersection over union (IOU) based verification.

Automatic Identification and Counting of Blood Cells

Dataset

The Complete Blood Count (CBC) Dataset has been used for automatic identification and counting of blood cells. Download the dataset, unzip and put the Training, Testing, and Validation folder in the working directory.

Requirements

• TensorFlow-GPU==1.11.0
• OpenCV==3.4.2
• Cython==0.29.2
• Weights: download the trained weights file for blood cell detection and put the weights folder in the working directory.

How to Run the Code

A step by step guideline of how to run the blood cell detection code in your computer is provided in this wiki.

Blood Cell Detection Output

KNN and IOU Based Verfication

In some cases, our model predicts the same platelet twice. To solve this problem we propose k-nearest neighbor (KNN) and intersection over union (IOU) based verification system where we find the nearest platelet of a platelet and calculate their overlap. We are allowing only 10% of 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 Verfication	After Verification

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

Paper

For more detail explanation, please go through the pdf of the paper. Cite this paper as:

@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}
}

How to Train on Your Dataset

A seven-step guideline of how to train on your own dataset is provided in this wiki.