ReviewComputational intelligence and neuroscience2022
Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.
Review in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Mesolens imaging in microbiology.Essays in biochemistry · 2026Review
- Semantic Explanation for Malaria Diagnosis: Comparing Human and Machine Generated Annotations forIEEE open journal of engineering in medicine and biology · 2026Article
- Intelligent automation cognitive system for accurate malaria diagnosis using digital blood smears.PloS one · 2026Article
- From genomes to interventions: computational strategies transforming parasitology.Frontiers in veterinary science · 2026Review
- Diagnosis ofJournal of clinical microbiology · 2025Article
- DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.BMC medical imaging · 2024Article
- Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.BMC medical imaging · 2024Article
- An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.Parasites & vectors · 2024Article
- Potential diagnostic application of a novel deep learning- based approach for COVID-19.Scientific reports · 2024Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.Open forum infectious diseases · 2023Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.
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