A deep learning approach for Malaria Parasite detection from Blood sample images

Many diseases are already being predicted with Machine Learning like fever and jaundice. The problem we targeted is about Malaria testing in rural areas. Globally, it takes a long time, due to the delay in sending physical reports from rural areas to distant labs in urban areas. Thinking about its s

Project Title

A deep learning approach for Malaria Parasite detection from Blood sample images

Project Area of Specialization

Artificial Intelligence

Project Summary

Many diseases are already being predicted with Machine Learning like fever and jaundice. The problem we targeted is about Malaria testing in rural areas. Globally, it takes a long time, due to the delay in sending physical reports from rural areas to distant labs in urban areas. Thinking about its solution from a software perspective, firstly we will need an efficient algorithm based on AI that is capable of predicting optimally whether a patient is infected with Malaria or not. Today, with the help of Deep Learning, we can solve this problem.

We will find relevant Deep Learning algorithms best suited for Malaria parasite detection. Then, compare multiple algorithms with performance-specific modifications needed for optimal results.

Our goal is to find the best performing algorithm for Malaria detection. This will allow us to predict malaria instantly instead of sending reports physically to distant labs.

Project Objectives

Using Deep Learning techniques to improve existing Malaria detection results from microscopic images

Project Implementation Method

Project Implementation phase comprises of finding relevant Deep Learning algorithms best suited for Malaria parasite detection, comparing multiple algorithms with performance-specific modifications needed for optimal results. The ultimate goal is to find the best performing algorithm for Malaria detection

Benefits of the Project

Provide assistance to researchers with an improved Malaria detection algorithm

Act as a conceptual prototype for hardware based projects about Malaria detection

Technical Details of Final Deliverable

Final Deliverable includes the following technical details:

1. Custom Implementation of Deep Learning models needed for object detection and classification of Malaria parasite (P.falciparum)

2. Results based on each model's Performance Metrics which include Accuracy, Precision, Recall and F1 Score

3. Research paper writeup

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com