Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 28, 2024Open Access

Malaria Cell Detection Using Deep Neural Networks

View Full Paper
Ask AI
Bookmark
Share

Authors

SSSaurabh S. SawantASAnurag Singh

Discussion

Loading...

Member takes

Overview

Diagnostic study demonstrates automated classification of infected red blood cells, highlighting scalable screening potential for resource-limited settings.

Key Points

  • Automated detection of malaria parasites demonstrated high classification accuracy and recall, offering an efficient alternative to manual microscopy.
  • Benchmark on 27,558 blood smear images using a ResNet50 convolutional neural network and transfer learning achieved robust detection across cell classes.
  • Streamlit web application deployment highlights potential for rapid point-of-care malaria diagnosis, supporting frontline staff in resource-limited clinics.

Cite This Study

Sawant et al. (2024) studied this question.

synapsesocial.com/papers/68e62c34b6db6435875bf092https://doi.org/10.48550/arxiv.2406.20005
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification2024
  2. 2Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells2024 · 6 citations
  3. 3MosquitoNet Based Deep Learning Approach for Malaria Parasite Detection Using Cell Images2024 · 5 citations
  4. 4Malaria Detection Using an Improved AlexNet-Based Deep Learning Model2026
  5. 5Malaria Parasite Detection in Microscopic Blood Smear Images using Deep Learning Approach2024 · 1 citations