/Malaria-Detection-using-Blood-Cells

Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the diagnosis.

Primary LanguageJupyter Notebook

Malaria-Detection-using-Blood-Cells

Open Source Love Code Climate


Logo

About The Project

Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the diagnosis.

In this project, I have designed & developed a deep learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. A ten-fold cross-validation layer of the convolutional neural network on 27,558 single-cell images is used to understand the parameter of the cell.

Libraries used :

  * pandas
  * numpy
  * matplotlib
  * keras
  * sklearn

Algorithms Used

  • ResNet50
  • DenseNet201 (Transfer Learning in Deep Learning)
  • VGG16

Directory Structure

├── images
│   ├── logo.jpg
│   └── mini-logo.png
├── Malaria_Parasite__Detection_using_Blood_cell.ipynb
├── malaria_parasite__detection_using_blood_cell.py
└── README.md

Getting Started

This is an example of how you may set up this project locally. To get a local copy up and running follow these simple steps.

Installation

  1. Clone the repo 
    git clone https://github.com/kanishksh4rma/Malaria-Detection-using-Blood-Cells.git
  2. Upload it on google colab and start using :)

About Contribution :

  • Raise the issue .
  • Work on raised issues .
  • Come up with interesting Medical related problems and solutions .
  • You can improve the UI/UX .
  • Can contribute on readme files as well .

Package Guidelines

See CONTRIBUTING.md file for detailed information.

License

See LICENSE file.

                                  "Take stands, take risks, take responsibility."
                                                                — Muriel Siebert

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