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Parasite Detection From Digital Images Using Deep Learning
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Author(s): Yulin Zhu (Auckland University of Technology, New Zealand)and Wei Qi Yan (Auckland University of Technology, New Zealand)
Copyright: 2023
Pages: 11
Source title:
Machine Learning and AI Techniques in Interactive Medical Image Analysis
Source Author(s)/Editor(s): Lipismita Panigrahi (GITAM University (Deemed), India), Sandeep Biswal (O.P. Jindal University, India), Akash Kumar Bhoi (KIET Group of Institutions, India & Sikkim Manipal University, India), Akhtar Kalam (Victoria University, Australia)and Paolo Barsocchi (Institute of Information Science and Technologies, Italy)
DOI: 10.4018/978-1-6684-4671-3.ch007
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Abstract
Parasitosis is a disease caused by parasites that could infect humans, animals, or plants. The parasites include mites, ascariasis, liver flukes, and malaria. The methods to detect parasites include pathological examination, immunological examination, and imaging examination. In this chapter, parasitic infections are detected from digital images acquired from a microscope, which will look for the possible infection caused by worms or eggs in a sample, such as mites and malaria. Rapid and accurate classification and detection of parasites will be very helpful for fast diagnosis and treatment. In this chapter, a malaria detection method is deployed by using deep learning based on TensorFlow and achieved 0.73 mAP@0.5IOU. Even if it does not seem to be a perfect performance, in the limited time and resources, the results are still valuable. The future work could port the model to mobile phones for image detection, which would bring much more convenience and portability.
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