BloodPy 🩸 Automated Blood Cell Classifier

Overview

This project aims to develop an automatic recognition system for classifying peripheral blood cell images into different cell types. The dataset used in this project consists of microscopic images of individual normal cells, acquired from the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized into eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes, erythroblasts, and platelets or thrombocytes.

Objective

Its primary goal is to leverage computer vision techniques to automate the process of analyzing blood smears and aid in diagnosis of various pathologies, such as leukemia. By accurately identifying and quantifying blood cell types, the tool can assist medical professionals in assessing the density and relative abundance of cells, which is crucial for diagnosing and monitoring certain diseases and facilitate research purposes.

Dataset Description

• Total Images: 17,092
• Image Size: 360 x 363 pixels (JPG format)
• Annotation: Expert clinical pathologists annotated the images.
• Categories: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes.
• Image Source: Individuals without infection, hematologic or oncologic disease, and free of any pharmacologic treatment at the moment of blood collection.

Environment Setup

To run this project, you will need to set up a Python environment that contains all the necessary libraries and dependencies. This project uses Conda for environment management.

Using the provided Conda environment file

1. Clone the repository or download the project files.

2. Navigate to the project directory where environment.yml is located.

3. Create the Conda environment by running the following command in your terminal:

   conda env create -f environment.yml
   

This command will create a new Conda environment with all the dependencies specified in the environment.yml file.

4. Activate the newly created environment by running:

   conda activate bloodCNN
   

5. Verify that the environment has been set up correctly and that all dependencies are installed by running:

   conda list
   

By following these steps, you should have a fully configured environment ready to run the project scripts.