@InProceedings{SoaresFaFaFaPaGo:2022:AuSpHe,
               author = "Soares, Marco Ant{\^o}nio Calijorne and Falci, Daniel Henrique 
                         Mour{\~a}o and Farnezi, Marco Fl{\'a}vio Alves and Farnezi, Hana 
                         Carolina Moreira and Parreiras, Fernando Silva and Gomide, 
                         Jo{\~a}o Victor Boechat",
          affiliation = "{FUMEC University} and {FUMEC University} and {FUMEC University} 
                         and {FUMEC University} and {FUMEC University} and {FUMEC 
                         University}",
                title = "Automated Sperm Head Morphology Classification with Deep 
                         Convolutional Neural Networks",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "infertility, sperm head classification, human sperm morphology, 
                         medical image classification, convolutional neural networks, deep 
                         learning.",
             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.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991745",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991745",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47LSPMH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47LSPMH",
           targetfile = "SIBIGRAPI_AutomatedSpermHeadMorphologyClassification_INPE.pdf",
        urlaccessdate = "2024, Apr. 28"
}