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
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
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Research Paper Publication | Equipment | 1 | 25000 | 25000 |
| Personal GPU Maintenance for training Deep Learning models | Equipment | 1 | 8000 | 8000 |
| Total in (Rs) | 33000 |
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