Deep Convolutional Neural Network Based Sperm Detection in Microscopic Videos

In recent years, with the increasingly serious quality problems of human sperm, the performance of traditional sperm analysis methods has been unable to meet the growing demand for diagnosis and treatment. The purpose of this study was to explore the application of deep convolutional neural network microvideo in computer assisted sperm analysis (CASA). For the sperm detection task, the model structure, loss function and non-maximum suppression algorithm were optimized to improve the performance of the model, reflecting the advantages of deep learning. To avoid the impact of data differences, cross-validation test was adopted in this study, and multiple indexes such as F1-score and AP were compared with the original model after training. These improvements significantly improved the average accuracy and F1 scores of the models. The performance of the model not only has an important impact on the indicators of the target detection model, but also has an important impact on the visualization results of the model.