Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection
| Autor: | Patryk Najgebauer, Leszek Rutkowski, Agnieszka Siwocha, Rafał Scherer, Rafał Grycuk |
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| Rok vydání: | 2019 |
| Předmět: |
Pixel
Computer science business.industry Sample (material) Less invasive Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences 0202 electrical engineering electronic engineering information engineering Parasite hosting 020201 artificial intelligence & image processing Segmentation Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | Artificial Intelligence and Soft Computing ISBN: 9783030209117 ICAISC (1) |
| DOI: | 10.1007/978-3-030-20912-4_16 |
| Popis: | This paper describes a method of pixel-level segmentation applied to parasite detection. Parasite diseases in most cases are detected by microscopic samples examination or by ELISA blood tests. The microscopic methods are less invasive and often used in veterinary, but they need more time to prepare and visually evaluate samples. Diagnosticians search the entire sample to find parasite eggs and to classify their species. Depending on the species of the diagnosed animal, the samples can contain various types of pollution, e.g. fragments of plants. Most of the objects in the sample by their transparency look similar, and some of parasites eggs might be unintentionally omitted. The presented method based on fully convolutional network allows processing the entire space of the sample and assigning a class to each pixel of the image. Our model was trained to classify parasite eggs and distinguish them from adjacent or overlapped pollution. |
| Databáze: | OpenAIRE |
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