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

AI-driven optimization: revolutionizing energy efficiency in modern buildings.

Scientific reports·2026

Same author

An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.

Diagnostics (Basel, Switzerland)·2026

Same author

A comprehensive review of deep learning applications in the segmentation and classification of skin cancer.

Biomedical physics & engineering express·2026

Same author

AI-Driven Microbial Diagnostics: Predicting Disease Signatures Through Microbial Pattern Recognition.

Diagnostics (Basel, Switzerland)·2026

Same author

MEMS-based near-infrared spectroscopy with AI for real-time breast cancer margin assessment.

Surgical oncology·2026

Same author

Natural products and dietary bioactives as antiviral agents against herpesviruses: molecular mechanisms, immunomodulatory actions, and nutritional perspectives.

Archives of microbiology·2026

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

Automated multi-model framework for malaria detection using deep learning and feature fusion.

Osama R Shahin¹, Hamoud H Alshammari², Raed N Alabdali³

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia. orshahin@ju.edu.sa.

Scientific Reports

|July 15, 2025

Summary

This study introduces an AI framework for automated malaria diagnosis, significantly improving accuracy and efficiency over traditional methods. The advanced system utilizes deep learning and machine learning for reliable detection in blood smear images.

Keywords:

AI solutions CNN Feature fusion Majority voting Malaria detection

More Related Videos

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

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 27, 2026

A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

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

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
Artificial Intelligence in Healthcare
Parasitology

Background:

Malaria diagnosis traditionally relies on microscopy, which can be time-consuming, labor-intensive, and prone to human error.
Existing diagnostic methods face limitations in accuracy and efficiency, particularly in resource-limited settings.
There is a critical need for advanced, automated solutions to improve malaria detection rates and patient outcomes.

Purpose of the Study:

To develop and validate an advanced, automated diagnostic framework for malaria detection.
To integrate deep learning and machine learning techniques for enhanced diagnostic accuracy and efficiency.
To establish a robust AI-driven system for reliable malaria identification from microscopic blood smear images.

Main Methods:

A multi-model architecture was designed, incorporating ResNet 50, VGG16, and DenseNet-201 for feature extraction via transfer learning.
Feature fusion and principal component analysis (PCA) were employed for dimensionality reduction.
A hybrid classification scheme combining support vector machine (SVM) and long short-term memory (LSTM) networks was utilized, with a majority voting ensemble for final prediction.

Main Results:

The proposed framework achieved high performance metrics on a dataset of 27,558 microscopic thin blood smear images.
Achieved accuracy of 96.47%, sensitivity of 96.03%, specificity of 96.90%, precision of 96.88%, and F1-score of 96.45% using the majority voting ensemble.
Demonstrated superior diagnostic reliability and computational efficiency compared to existing malaria detection methods.

Conclusions:

The AI-driven framework offers a significant advancement in automated malaria diagnostics.
The study highlights the potential of integrated deep learning and machine learning for improving blood-borne disease detection.
This research provides a foundation for developing AI solutions for other infectious diseases.