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Parasitic Egg Detection and Classification in Low-Cost Microscopic Images Using Transfer Learning

https://doi.org/10.1007/s42979-023-02406-8

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jbio.201800410openalex-snapshot

A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

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SSD: Single Shot MultiBox Detector

inferredcites →https://doi.org/10.1007/978-3-319-65981-7_12openalex-snapshot

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

inferredcites →https://doi.org/10.1007/978-981-13-9042-5_5openalex-snapshot

Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples

inferredcites →https://doi.org/10.1007/s10462-021-10039-7openalex-snapshot

Artificial intelligence in the creative industries: a review

inferredcites →https://doi.org/10.1016/j.eswa.2007.09.012openalex-snapshot

An expert diagnosis system for classification of human parasite eggs based on multi-class SVM

inferredcites →https://doi.org/10.1038/s41598-021-89863-4openalex-snapshot

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN

inferredcites →https://doi.org/10.1080/16549716.2017.1337325openalex-snapshot

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

inferredcites →https://doi.org/10.1109/10.923789openalex-snapshot

Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/icip46576.2022.9897267openalex-snapshot

ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

inferredcites →https://doi.org/10.1109/iscaie.2012.6482095openalex-snapshot

Human parasitic worm detection using image processing technique

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1145/3310986.3311014openalex-snapshot

Parasite worm egg automatic detection in microscopy stool image based on Faster R-CNN

inferredcites →https://doi.org/10.1201/b19241-19openalex-snapshot

Diagnosis of human intestinal parasites by deep learning

inferredcites →https://doi.org/10.48550/arxiv.1608.02989openalex-snapshot

Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics

inferredcites →https://doi.org/10.48550/arxiv.2004.10934openalex-snapshot

YOLOv4: Optimal Speed and Accuracy of Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2106.00389openalex-snapshot

Analysis of Vision-based Abnormal Red Blood Cell Classification

inferredcites →https://doi.org/10.5539/mas.v7n5p98openalex-snapshot

Automated System for Diagnosis Intestinal Parasites by Computerized Image Analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Artificial Intelligence-Assisted Segmentation and Classification of Enterobius Vermicularis

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Parasitic Egg Detection and Classification: A Systematic Review

inferred← citeshttps://doi.org/10.2139/ssrn.5367031openalex-extracted

Paravisionnet: A Multitask Vision Transformer Framework for Accurate Detection and Classifi-Cation of Parasitic Eggs in Microscopy Images

inferred← citeshttps://doi.org/10.1007/s10489-024-06199-yopenalex-snapshot

DT4PEIS: detection transformers for parasitic egg instance segmentation

inferred← citeshttps://doi.org/10.1007/s11356-025-36343-1openalex-snapshot

Environmental monitoring for Ascaris egg remediation: a critical review

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Automated Monitoring and Active Control of Jet-Breakup Ceramic-Pebble Fabrication

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2026.110978openalex-snapshot

ParaVisionNet: A multitask vision transformer framework for accurate detection and classification of parasitic eggs in microscopy images

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2026.111492openalex-snapshot

A calibrated multi-scale transformer-based dual deep convolutional neural network framework for robust parasitic egg recognition across diverse microscopy conditions

inferred← citeshttps://doi.org/10.1016/j.health.2025.100432openalex-snapshot

An artificial intelligence-based approach for human parasite egg segmentation and classification

inferred← citeshttps://doi.org/10.1016/j.ibmed.2025.100212openalex-snapshot

Comparative analysis of resource-efficient YOLO models for rapid and accurate recognition of intestinal parasitic eggs in stool microscopy

inferred← citeshttps://doi.org/10.1016/j.ijpara.2025.08.007openalex-snapshot

A dedicated deep learning workflow for automatic Fasciola hepatica and Calicophoron daubneyi egg detection using the Kubic FLOTAC microscope

inferred← citeshttps://doi.org/10.1016/j.microc.2025.116647openalex-snapshot

Machine learning–guided scanning electrochemical microscopy on a calpastatin-modified ITO biosensor for label-free detection of Schistosoma haematobium eggs

inferred← citeshttps://doi.org/10.1016/j.parepi.2026.e00545openalex-snapshot

Automated detection and classification of intestinal protozoan cysts using a nuclei structure-based deep learning framework for epidemiological surveillance

inferred← citeshttps://doi.org/10.1016/j.procs.2025.02.174openalex-snapshot

Neural Network Ensemble for Detecting Parasite Eggs in Microscopic Images

inferred← citeshttps://doi.org/10.1038/s41598-023-41711-3openalex-snapshot

Parasitic egg recognition using convolution and attention network

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RETRACTED ARTICLE: Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images

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Parasite.ai – An Automated Parasitic Egg Detection Model from Microscopic Images of Fecal Smears using Deep Learning Techniques

inferred← citeshttps://doi.org/10.1109/access.2024.3494249openalex-snapshot

Adaptive Enhancement Network With Border Injection for Animal Parasite Eggs Detection

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Evaluation of Three Different Operators for Object Highlighting in Medical RGB Images: Canny, Roberts, and LoG in Independent Color Spaces

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Automated detection of Enterobius vermicularis eggs in microscopic images using YOLOv8

inferred← citeshttps://doi.org/10.1109/cce60043.2023.10332855openalex-snapshot

Parasite Detection in Copro Images with a modified Faster R-CNN

inferred← citeshttps://doi.org/10.1109/icecte57896.2022.10114481openalex-snapshot

Comparative Study of the Effect of Various Color Models on Classification of Parasitic Eggs

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897267openalex-snapshot

ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897645openalex-snapshot

Parasitic Egg Detection and Classification by Utilizing the YOLO Algorithm with Deep Latent Space Image Restoration and GrabCut Augmentation

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897844openalex-snapshot

Localization and Classification of Parasitic Eggs in Microscpic Images Using An Efficientdet Detector

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897846openalex-snapshot

Parasitic Egg Detection and Classification with Transformer-Based Architectures

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897858openalex-snapshot

Parasitic Egg Detection with a Deep Learning Ensemble

inferred← citeshttps://doi.org/10.1109/icitee59582.2023.10317719openalex-snapshot

Development of Self-Supervised Learning with Dinov2-Distilled Models for Parasite Classification in Screening

inferred← citeshttps://doi.org/10.1109/icivc66358.2025.11200451openalex-snapshot

P2P-NET: A PSO-Vision Framework for Accurate Detection and Multi-Class Classification of Parasitic Eggs in Human and Animal in Microscopy Images

inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11228975openalex-snapshot

IPI-CVx: Explainable and Robust Detection of Intestinal Parasites Using Augmentation-Based Supervised Target Learning

inferred← citeshttps://doi.org/10.1109/ivit62102.2024.10692960openalex-snapshot

A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

inferred← citeshttps://doi.org/10.1109/les.2023.3267903openalex-snapshot

Automatic Fecal Eggs Counting in Ruminants Using Xilinx DPU

inferred← citeshttps://doi.org/10.1371/journal.pntd.0014774openalex-snapshot

Deep learning applications in parasite microscopy: A scoping review

inferred← citeshttps://doi.org/10.14569/ijacsa.2024.0150590openalex-snapshot

Automatic Detection of Ascaris Lumbricoides in Microscopic Images using Convolutional Neural Networks (CNN)

inferred← citeshttps://doi.org/10.17979/spudc.9788497498418.1023openalex-snapshot

Detección y clasificación de huevos de parásitos en imágenes microscópicas

inferred← citeshttps://doi.org/10.20460/jgsm.2023.319openalex-snapshot

USING DEEP LEARNING MODELS TO DETECT PARASITES EARLY

inferred← citeshttps://doi.org/10.20965/jaciii.2023.p1113openalex-snapshot

An Approach for Egg Parasite Classification Based on Ensemble Deep Learning

inferred← citeshttps://doi.org/10.33003/jobasr-2024-v2i1-42openalex-snapshot

Enhancing Bacteria Classification using Image Processing and Convolutional Neural Network

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Automating parasite egg detection: insights from the first AI-KFM challenge

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An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5

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Automated Detection of Parasitic Elements in Veterinary Fecal Samples Using a Deep Learning-Based Object Detection Framework

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Detection of Parasitic Eggs Using Deep Learning: A Survey

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