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

• URL: http://arxiv.org/abs/2203.02940v1
• Date: Sun, 6 Mar 2022 11:44:35 GMT
• Title: Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset
• Authors: Perla Mayo, Nantheera Anantrasirichai, Thanarat H. Chalidabhongse, Duangdao Palasuwan and Alin Achim
• Abstract summary: Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts. We exploit successful architectures for detection, adapting them to tackle a different domain. We demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection.
• Score: 8.957918272018045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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