# Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning

arXiv: https://arxiv.org/abs/2107.00968 · HTML: https://mlsanity.com/paper/2107.00968/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 2 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Artificial Intelligence in the Creative Industries: A Review](https://mlsanity.com/paper/2007.12391/) (2020)
- [Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics](https://mlsanity.com/paper/1608.02989/) (2016)
- [Analysis of Vision-based Abnormal Red Blood Cell Classification](https://mlsanity.com/paper/2106.00389/) (2021)

## Built on by (most cited first, 3 of 3)

- [Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector](https://mlsanity.com/paper/2208.01963/) (2022)
- [Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset](https://mlsanity.com/paper/2203.02940/) (2022)
- [ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results](https://mlsanity.com/paper/2208.06063/) (2022)

## Abstract (arXiv)

Intestinal parasitic infection leads to several morbidities to humans worldwide, especially in tropical countries. The traditional diagnosis usually relies on manual analysis from microscopic images which is prone to human error due to morphological similarity of different parasitic eggs and abundance of impurities in a sample. Many studies have developed automatic systems for parasite egg detection to reduce human workload. However, they work with high quality microscopes, which unfortunately remain unaffordable in some rural areas. Our work thus exploits a benefit of a low-cost USB microscope. This instrument however provides poor quality of images due to limitation of magnification (10x), causing difficulty in parasite detection and species classification. In this paper, we propose a CNN-based technique using transfer learning strategy to enhance the efficiency of automatic parasite classification in poor-quality microscopic images. The patch-based technique with sliding window is employed to search for location of the eggs. Two networks, AlexNet and ResNet50, are examined with a trade-off between architecture size and classification performance. The results show that our proposed framework outperforms the state-of-the-art object recognition methods. Our system combined with final decision from an expert may improve the real faecal examination with low-cost microscopes.