Aim:
Feasibility study of commercial off-the-shelf (COTS) artificial intelligence and machine learning (AI/ML) solutions for automated cell isolation and identification of drug-treated malaria parasites from fluorescent microscope images to prepare them for future AI/ML-based phenotypic analysis.
Objectives:
- Using a pre-acquired dataset of raw microscope images of malaria parasites, evaluate and optimise AI/ML algorithms to efficiently and accurately extract cells of interest.
- Determine which AI/ML approach is most appropriate for this problem.
Apply solution to a newly generated dataset.
