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Blood Cell Detection using TensorFlow Object Detection API

Sayak Paul

Sayak Paul

Kolkata, West Bengal

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This project demonstrates the use of TensorFlow Object Detection API (along with GCP ML Engine) to automatically detect Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets in each image taken via microscopic image readings. ...learn more

Project status: Under Development

Artificial Intelligence

Intel Technologies
Intel Python

Code Samples [1]

Overview / Usage

This project demonstrates the use of TensorFlow Object Detection API (along with GCP ML Engine) to automatically detect Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets in each image taken via microscopic image readings.

The dataset used in this project was collected from here. Note that I deleted some of the files from the original dataset directory which I found out to be unnecessary for the project.

Methodology / Approach

I followed the official TensorFlow Object Detection API documentation and this article to kickstart the training process on GCP using ML Engine and Cloud TPUs and also to export the inference graph.

I used a Faster R-CNN based architecture since it resolves the problem of selective search pretty elegantly and yields a pretty good accuracy. The Model\_Checkpoints folder contains the latest checkpointed files collected from the training process.

Technologies Used

  • TensorFlow 1.12

  • TensorFlow Object Detection API

  • Google Cloud Platform's ML Engine

Repository

https://github.com/sayakpaul/Blood-Cell-Detection-using-TFOD-API

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