Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset

Autor:	Mayo, Perla, Anantrasirichai, Nantheera, Chalidabhongse, Thanarat H., Palasuwan, Duangdao, Achim, Alin
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ensure a prompt treatment to patients, and off-load excessive work from experts' shoulders. Advances in deep learning inspired us to exploit successful architectures for detection, adapting them to tackle a different domain. We propose a framework that exploits two such state-of-the-art models. Specifically, we demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection respectively, on microscopy images of varying quality. The use of these techniques yields encouraging results, though further improvements are still needed for certain egg types whose detection still proves challenging. As a result, a new dataset has been created and made publicly available, providing an even wider range of classes and variability. Comment: 7 pages, 3 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.02940 Zobrazit plný text záznamu View this record from Arxiv