MALARIA DETECTION USING MACHINE LEARNING

Malaria is a harmful disease and people affects from this disease around the world.A bactaria, named as Plasmodium cause Malaria. These bactaria are transmitted to aperson by the bites of infected female mosquito. The malaria detection test are costly,time consuming and inaccessible,especially in re

2025-06-28 16:34:04 - Adil Khan

Project Title

MALARIA DETECTION USING MACHINE LEARNING

Project Area of Specialization Artificial IntelligenceProject Summary

Malaria is a harmful disease and people affects from this disease around the world.A bactaria, named as Plasmodium cause Malaria. These bactaria are transmitted to aperson by the bites of infected female mosquito. The malaria detection test are costly,time consuming and inaccessible,especially in remote area.

The propsed system is an android applicationfor Malaria Detection using Artificial Intelligence to overcome the above mentioned problem. The android application will be used to pass microscopic image of a patient's blood to machine learning based server which will already be trained for detection of Malaria. After detection process results either positive or negative, will be sent back to the the Android application.

This application will be useful for epidemic situations and also for domestic purposes. This application will reduce the cost because lab equipments and chemicals used to detect malaria will not be required.This application will reduce human errors, and human effort.

Project Objectives

Malaria Detection using Artificial Intelligence (Machine Learning).

The should have:

   1) Ease of access

    2) Cost effective

    3) Less time consumption

Project Implementation Method

Dataset (Microscopic Images of blood) of the project will be obtained from Kaggle.

Machine Learning Algorithm (Convolutional Neural Networks) will be used to detect Malaria.

Training of Model will be done using dataset on cloud server.

Microscopic lens will be used to capture microscopic image of blood cells.

Anndroid Applicatoion will be used to pass microscopic image to the cloud server for malaria detection.

Benefits of the Project

Useful for Epidemic situation,Domestic Purposes.

Solution for Doctors,Users with microscopic blood images.

Low Cost Solution as no lab equipments and technicians cost required.

Less Human effort,less human errors and less time

This model may apply on other diseases.

Technical Details of Final Deliverable

Android Application

Microscopic lens for Mobile

Complete documentation of project

Complete source code of project

Final Deliverable of the Project HW/SW integrated systemCore Industry MedicalOther Industries Health Core Technology Artificial Intelligence(AI)Other Technologies OthersSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources
Item Name Type No. of Units Per Unit Cost (in Rs) Total (in Rs)
Total in (Rs) 48500
Cloud Services Equipment11350013500
Microscopic Lens Equipment12500025000
Miscellaneous Miscellaneous 11000010000

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