Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review
importance 7/10
ai assisted
Digital Imaging for Blood Diseases malaria diagnostics stage: research
Problem
Malaria diagnosis by light microscopy is slow and requires rare expert pathologists, delaying timely treatment.
Why it matters
Malaria affects millions worldwide with a consistent mortality rate, so early diagnosis is life-critical.
What the AI did
The paper is a PRISMA-based systematic review of published computer-aided diagnosis systems using machine learning and deep learning to detect and classify malaria-infected blood cells in smear images.
Results
not stated in source
Impact
The review maps existing AI approaches to automated malaria detection but demonstrates no new result itself.
Record
| Venue | Electronics |
|---|---|
| Published | 2024 |
| Work type | review |
| Authors | Flavia Grignaffini, Patrizio Simeoni, Anna Alisi, Fabrizio Frezza |
| Citations | 22 (OpenAlex, at import time) |
| References | 199 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/electronics13163174 |
| Open access | gold |
| Keywords | malaria, computer-aided diagnosis, deep learning, blood smear, systematic review |
Importance rating
7
significant — thousands to millions affected
Abstract states malaria affects millions worldwide with consistent mortality; early diagnosis is life-critical, though the paper is only a literature review.