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Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network

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Computer-Human Interaction Research and Applications (CHIRA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 654))

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Abstract

We develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone). The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation.

V. B. Guilherme, M. Pronunciate and P. H. dos Santos—Authors contributed equally to the study.

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Acknowledgement

The author’s research is supported by grants #2012/50533-2, 2013-05083-1, 2006/06491-2, 2011/06179-7, 2012/20110-2 and 2016/19004-4 from São Paulo Research Foundation (FAPESP). We also thank Agência UNESP de Inovação (AUIN) for processing the national and international patents of the invention.

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Authors and Affiliations

  1. Laboratório de Micromanipulação Embrionária, School of Sciences and Languages, Universidade Estadual Paulista (Unesp), Av. Dom Antonio 2100, Assis, Brazil

    Vitória Bertogna Guilherme, Priscila Helena dos Santos & Marcelo Fábio Gouveia Nogueira

  2. Laboratório Multiusuário FitoFarmaTec, Institute of Biosciences, Unesp, Rubião Jr., Botucatu, Brazil

    Micheli Pronunciate & Marcelo Fábio Gouveia Nogueira

  3. Laboratório de Matemática Aplicada, School of Sciences and Languages, Unesp, Av. Dom Antonio 2100, Assis, Brazil

    Diego de Souza Ciniciato, Maria Beatriz Takahashi & José Celso Rocha

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  1. Vitória Bertogna Guilherme
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  2. Micheli Pronunciate
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  3. Priscila Helena dos Santos
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  4. Diego de Souza Ciniciato
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Correspondence to Marcelo Fábio Gouveia Nogueira .

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  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. IT- Institute of Telecommunications, Lisbon, Portugal

    Hugo Plácido Silva

  3. Dublin City University, Dublin, Ireland

    Markus Helfert

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Guilherme, V.B. et al. (2019). Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network. In: Holzinger, A., Silva, H., Helfert, M. (eds) Computer-Human Interaction Research and Applications. CHIRA 2017. Communications in Computer and Information Science, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-030-32965-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-32965-5_8

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