Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

9 Oct 2024

Abstract excerpt

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Malaria Research and Control
Share to Mosquito-borne diseases and control

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39384771
DOI: 10.1038/s41598-024-71856-8

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 9 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identificationsPMID 39384771Read ↗

Transforming Mosquito Surveillance LarvaFormer as a Hybrid Deep Learning Tool for Rapid and Accurate Larval RecognitionSemantic proximity Read ↗

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networksSemantic proximity Read ↗

Unveiling the Optimal CNN- A Performance Evaluation of Architectures in Malaria Image ClassificationSemantic proximity Read ↗

Toward a Methodology for Mosquito Egg Detection and Counting Using Standardized Ovitrap Data in Vector SurveillanceSemantic proximity Read ↗

Lightweight Deep Learning Models for Efficient Classification of Plant-Parasitic Nematodes on Resource-Constrained DevicesSemantic proximity Read ↗

Identification of public submitted tick images: a neural network approachSemantic proximity Read ↗

Rapid ageing and species identification of natural mosquitoes for malaria surveillanceSemantic proximity Read ↗

Application of Wings Interferential Patterns (WIPs) and Deep Learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importanceSemantic proximity Read ↗

FlightTrackAI: a convolutional neural network-based software for tracking the flight behaviour of Aedes aegypti mosquitoesSemantic proximity Read ↗

The MosAICo ecosystem: bridging the taxonomic gap in vector surveillance with real-time entomological artificial intelligenceSemantic proximity Read ↗

A Lightweight Deep Learning Framework for Reliable Microscopy-Based Diagnosis of Cutaneous LeishmaniasisSemantic proximity Read ↗

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex speciesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.