%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2022/09.22.19.28
%2 sid.inpe.br/sibgrapi/2022/09.22.19.28.04
%@doi 10.1109/SIBGRAPI55357.2022.9991745
%T Automated Sperm Head Morphology Classification with Deep Convolutional Neural Networks
%D 2022
%A Soares, Marco Antônio Calijorne,
%A Falci, Daniel Henrique Mourão,
%A Farnezi, Marco Flávio Alves,
%A Farnezi, Hana Carolina Moreira,
%A Parreiras, Fernando Silva,
%A Gomide, João Victor Boechat,
%@affiliation FUMEC University
%@affiliation FUMEC University
%@affiliation FUMEC University
%@affiliation FUMEC University
%@affiliation FUMEC University
%@affiliation FUMEC University
%B Conference on Graphics, Patterns and Images, 35 (SIBGRAPI)
%C Natal, RN
%8 24-27 Oct. 2022
%S Proceedings
%K infertility, sperm head classification, human sperm morphology, medical image classification, convolutional neural networks, deep learning.
%X 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.
%@language en
%3 SIBIGRAPI_AutomatedSpermHeadMorphologyClassification_INPE.pdf