Malaria-net

Introduction

Malaria is a mosquito-borne infectious disease that affects humans and other animals. Malaria causes symptoms that typically include fever, tiredness, vomiting, and headaches. In severe cases, it can cause yellow skin, seizures, coma, or death. Symptoms usually begin ten to fifteen days after being bitten by an infected mosquito. If not properly treated, people may have recurrences of the disease months later. In those who have recently survived an infection, reinfection usually causes milder symptoms. This partial resistance disappears over months to years if the person has no continuing exposure to malaria.

Dataset Content

The dataset contains 2 folders

• Infected
• Uninfected , with total of 27,558 images.

Acknowledgements

This Dataset is taken from the official NIH Website: https://ceb.nlm.nih.gov/repositories/malaria-datasets/ And uploaded here, so anybody trying to start working with this dataset can get started immediately, as to download the dataset from NIH website is quite slow.

Photo by Егор Камелев on Unsplash https://unsplash.com/@ekamelev

Inspiration

Save humans by detecting and deploying Image Cells that contain Malaria or not!

Uninfected images in BGR channel

Infected or images in BGR channel

Libraries used in this project:

• Numpy -- for matrix and array related operations
• Matplotlib -- for plotting
• Opencv -- for image processings opperation like blurring,thresholding etc etc.
• Keras -- for Deep learning architecture building

My Sequential model for binary classification in Keras

My Sequential model performance in training set

GRADCAM Algorithim

For this classification model GRADCAM results

Tensorflow object detecton API result. Model used here is SSD