Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Malaria

Malaria

Malaria pathogenesis in humans reflects a delicate interplay between parasite biology and host response. Clinical illness reflects a host’s immune response to the parasite’s asexual replication cycle, which is often asymptomatic in individuals with partial immunity. From the parasite's perspective, transmission between mosquito and human with minimal host pathology is evolutionarily advantageous. Among the six Plasmodium species infecting humans, P. falciparum and P. vivax dominate in global...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties.

NMR in biomedicine·2022

Same author

Performance Evaluation of SeisTutor Using Cognitive Intelligence-Based "Kirkpatrick Model".

Computational intelligence and neuroscience·2022

Same author

Speech as a Biomarker for COVID-19 Detection Using Machine Learning.

Computational intelligence and neuroscience·2022

Same author

Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies.

Computational intelligence and neuroscience·2022

Same author

Learning-based motion artifact removal networks for quantitative <math></math> mapping.

Magnetic resonance in medicine·2022

Same author

The Role of the Human Brain Neuron-Glia-Synapse Composition in Forming Resting-State Functional Connectivity Networks.

Brain sciences·2021

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Deep Learning and Transfer Learning for Malaria Detection.

Tayyaba Jameela¹, Kavitha Athotha¹, Ninni Singh^2,3

¹Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad, India.

Computational Intelligence and Neuroscience

|July 11, 2022

Summary

This summary is machine-generated.

Automating malaria diagnosis using deep learning significantly improves accuracy over manual microscopy. Convolutional neural networks, particularly VGG-19, show promise in identifying Plasmodium parasites in blood slides.

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 29, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Medical diagnostics
Computational biology
Infectious disease research

Background:

Malaria is a deadly infectious disease causing over 500,000 deaths annually.
Delayed or incorrect diagnosis is a primary cause of malaria-related mortality.
Current manual microscopy for malaria diagnosis is time-consuming and error-prone.

Purpose of the Study:

To advocate for the automation of malaria diagnosis.
To reduce human error and improve diagnostic speed and accuracy.
To explore the efficacy of deep learning models for malaria detection.

Main Methods:

Utilized convolutional neural networks (CNNs) and image processing for automated diagnosis.
Trained CNN models (ResNet50, ResNet34, VGG-16, VGG-19) on microscopic blood slide images.
Employed transfer learning and fine-tuning techniques for model optimization.

Main Results:

Evaluated model performance based on intensity characteristics of Plasmodium parasites and erythrocytes.
VGG-19 demonstrated the highest overall performance among the tested CNN models.
Deep learning approaches enhance diagnostic accuracy for malaria detection.

Conclusions:

Automation of malaria diagnosis using deep learning offers a significant advancement.
CNNs, especially VGG-19, provide a reliable method for evaluating parasitemia.
Automated systems can overcome limitations of manual microscopy, improving global health outcomes.