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		<identifier>8JMKD3MGPEW34M/47LSPMH</identifier>
		<repository>sid.inpe.br/sibgrapi/2022/09.22.19.28</repository>
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		<doi>10.1109/SIBGRAPI55357.2022.9991745</doi>
		<citationkey>SoaresFaFaFaPaGo:2022:AuSpHe</citationkey>
		<title>Automated Sperm Head Morphology Classification with Deep Convolutional Neural Networks</title>
		<shorttitle>Automated Sperm Head Morphology Classification</shorttitle>
		<format>On-line</format>
		<year>2022</year>
		<numberoffiles>1</numberoffiles>
		<size>312 KiB</size>
		<author>Soares, Marco Antônio Calijorne,</author>
		<author>Falci, Daniel Henrique Mourão,</author>
		<author>Farnezi, Marco Flávio Alves,</author>
		<author>Farnezi, Hana Carolina Moreira,</author>
		<author>Parreiras, Fernando Silva,</author>
		<author>Gomide, João Victor Boechat,</author>
		<affiliation>FUMEC University</affiliation>
		<affiliation>FUMEC University</affiliation>
		<affiliation>FUMEC University</affiliation>
		<affiliation>FUMEC University</affiliation>
		<affiliation>FUMEC University</affiliation>
		<affiliation>FUMEC University</affiliation>
		<e-mailaddress>jvictor@fumec.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 35 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal, RN</conferencelocation>
		<date>24-27 Oct. 2022</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>infertility, sperm head classification, human sperm morphology, medical image classification, convolutional neural networks, deep learning.</keywords>
		<abstract>Background and Objective: The morphological analysis of sperm cells is considered a tool in human fertility prognosis. However, this process is manual, time-consuming and dependent on professional expertise. From a computational perspective, this is a challenging problem due to the high intercategory similarity between the objects of interest and the amount of data available. In this paper, we propose a Convolutional Neural Network model to automate morphology analysis of human sperm heads. Methods: We performed K-Fold cross-validation experiments over two publicly available datasets and assessed the performance of the proposed approach using Accuracy, Precision, Recall and F1-Score.We also compared the proposed model with well-known Convolutional architectures and previous approaches on the same task. Results: Experimental evaluation showed that our approach achieved a macro-averaged F1-score of 0.95 while our best model attained an accuracy of 97.7%. The error analysis revealed a balanced classifier over different sperm head classes. Conclusions: We proved that the proposed approach outperformed the previous state-of-the-art results on this task.</abstract>
		<language>en</language>
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