AI-Driven Automated Malaria Diagnostics with Smartphone Microscopy CSR Project

AI-driven automated malaria diagnostics from thick/thin blood images in field

Objective

• a. Developing Smartphone based Microscopes and adapters for acquiring images, b. Developing edge AI pipeline for automatically detecting malaria, identifying the parasite types and life-cycle stage, and paras, c. Generating malaria slide images in the field conditions, d. Demonstrating the developed solutions for automatic malaria detection in the field.

Description

• Poor and delayed malaria diagnosis is one of the reasons for the delay in the treatment of malaria in remote and rural parts of the nation in spite of the simplicity of the malaria diagnostic test., Artificial Intelligence (AI) based approaches to analyze the slide images can help automatically detect malaria parasites species and their life stages., The main objective of this proposal is to develop a deep learning approach for automatically detecting malaria-infected slide images obtained either from a laboratory microscope or smart-phone based microscope.

Impact

• It is proposed to develop an edge AI pipeline that will provide the following outputs: prediction of whether the given slide has malaria parasite or not., identify the types of human Plasmodium species., life cycle stage of species., parasite load in the sample.