Vision-based malaria parasite image analysis: a systematic review

Background: Malaria is one of the classic neglected serious diseases in many developing countries. The early stage of disease detection, accurate parasite count, detection of the aggressiveness of the disease, technical limitations, lack of expertise in malaria diagnosis and smart tools, lack of good quality healthcare services, funds so on are the challenges found during malaria diagnosis that requires a deeper analysis. Objectives: This paper aims to give a review of the automated diagnosis or visual inspection of malaria parasites using histology images of thin or thick blood film smears. Methods and Results: Various computer -aided diagnosis techniques are in use to solve tasks meticulously in a stratified description paradigm using non-linear transformation architectures. Conclusion: This work elaborates a comprehensive study of various computer vision diagnostic approaches already proposed in this field with a future direction for better quicker malaria identification.

• Publication year

  2019

• Venue

  International Journal of Bioinformatics Research and Applications

• Publication date

  2019-02-27

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1504/IJBRA.2019.097987
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

• Automatic Detection and Classification of Diabetic Retinopathy from Optical Coherence Tomography Angiography Images using Deep Learning - A Review

  2025cites this paper
• Unveiling the combined thermal and high strain rate effects on compressive behavior of steel fiber-reinforced concrete: A novel predictive approach

  2025cites this paper
• Genetic Programming-based Algorithms Application in Modeling the Compressive Strength of Steel Fiber-Reinforced Concrete Exposed to Elevated Temperatures

  2024cites this paper
• A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation

  2024cites this paper
• Deep learning and machine learning for Malaria detection: overview, challenges and future directions

  2022cites this paper
• ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification

  2021cites this paper
• Do we really need sub-micron resolution to analyse single cell molecular features through vibrational spectroscopy? A pilot study using Plasmodium falciparum-infected Human Erythrocytes

  2021cites this paper
• Comparing infrared spectroscopic methods for the characterization of Plasmodium falciparum-infected human erythrocytes

  2021cites this paper
• A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

  2021cites this paper
• Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM

  2019cites this paper
• Image Classification using Deep Neural Networks for Malaria Disease Detection

  2019cites this paper