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Joao LM, Proença LR, Loiola SHN, Inácio SV, Dos Santos BM, Rosa SL, Soares FA, Stefano VC, Osaku D, Suzuki CTN, Bresciani KDS, Gomes JF, Falcão AX. Toward automating the diagnosis of gastrointestinal parasites in cats and dogs. Comput Biol Med 2023; 163:107203. [PMID: 37437360 DOI: 10.1016/j.compbiomed.2023.107203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 06/25/2023] [Indexed: 07/14/2023]
Abstract
Diagnosing gastrointestinal parasites by microscopy slide examination often leads to human interpretation errors, which may occur due to fatigue, lack of training and infrastructure, presence of artifacts (e.g., various types of cells, algae, yeasts), and other reasons. We have investigated the stages in automating the process to cope with the interpretation errors. This work presents advances in two stages focused on gastrointestinal parasites of cats and dogs: a new parasitological processing technique, named TF-Test VetPet, and a microscopy image analysis pipeline based on deep learning methods. TF-Test VetPet improves image quality by reducing cluttering (i.e., eliminating artifacts), which favors automated image analysis. The proposed pipeline can identify three species of parasites in cats and five in dogs, distinguishing them from fecal impurities with an average accuracy of 98,6%. We also make available the two datasets with images of parasites of dogs and cats, which were obtained by processing fecal smears with temporary staining using TF-Test VetPet.
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Affiliation(s)
- L M Joao
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Letícia Rodrigues Proença
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Saulo Hudson Nery Loiola
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Sandra Valéria Inácio
- School of Veterinary Medicine, São Paulo State University (UNESP), R. Clóvis Pestana, Araçatuba, 16050-680, São Paulo, Brazil.
| | - Bianca Martins Dos Santos
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Stefany Laryssa Rosa
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Felipe Augusto Soares
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Vitória Castilho Stefano
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Daniel Osaku
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Celso Tetsuo Nagase Suzuki
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Katia Denise Saraiva Bresciani
- School of Veterinary Medicine, São Paulo State University (UNESP), R. Clóvis Pestana, Araçatuba, 16050-680, São Paulo, Brazil.
| | - Jancarlo Ferreira Gomes
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Alexandre Xavier Falcão
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
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Sousa AM, Castelo-Fernandez C, Osaku D, Bagatin E, Reis F, Falcao AX. An Approach for Asbestos-related Pleural Plaque Detection. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:1343-1346. [PMID: 33018237 DOI: 10.1109/embc44109.2020.9176605] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Asbestos is a toxic ore widely used in construction and commercial products. Asbestos tends to dissolve into fibers and after years inhaling them, these fibers calcify and form plaques on the pleura. Despite being benign, pleural plaques may indicate an immunologic deficiency or dysfunctional lung areas. We propose a pipeline for asbestos-related pleural plaque detection in CT images of the human thorax based on the following operations: lung segmentation, 3D patch selection along the pleura, a convolutional neural network (CNN) for feature extraction, and classification by support vector machines (SVM). Due to the scarcity of publicly available and annotated datasets of pleural plaques, the proposed CNN relies on architecture learning with random weights obtained by a PCA-based approach instead of using traditional filter learning by backpropagation. Experiments show that the proposed CNN can outperform its counterparts based on backpropagation for small training sets.
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Osaku D, Cuba CF, Suzuki CTN, Gomes JF, Falcão AX. Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits. Comput Biol Med 2020; 123:103917. [PMID: 32768052 DOI: 10.1016/j.compbiomed.2020.103917] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 07/12/2020] [Accepted: 07/13/2020] [Indexed: 11/27/2022]
Abstract
Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties: (DS1) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and (DS2) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. DS1 is much faster than DS2, but it is less accurate than DS2. Fortunately, the errors of DS1 are not the same of DS2. During training, we use a validation set to learn the probabilities of misclassification by DS1 on each class based on its confidence values. When DS1 quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by DS2. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine - a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.
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Affiliation(s)
- D Osaku
- Institute of Computing, University of Campinas, Brazil.
| | - C F Cuba
- Institute of Computing, University of Campinas, Brazil.
| | - C T N Suzuki
- Institute of Computing, University of Campinas, Brazil.
| | - J F Gomes
- Institute of Computing, University of Campinas, Brazil.
| | - A X Falcão
- Institute of Computing, University of Campinas, Brazil.
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