Morphological Classification of Low Quality Sperm Images Using Deep Learning Networks

Yuzkat, Mecit; İlhan, Hamza Osman; Aydın, Nizamettin

doi:10.1109/tiptekno50054.2020.9299318

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…With the rapid development of deep learning, it has been widely used in medical image processing (Hidayatullah et al, 2019; Tortumlu and Ilhan, 2020;Yüzkat et al, 2020), among which target detection networks and semantic segmentation networks have also been applied to detect sperm targets in images. Movahed and Orooji (2018) used a convolutional neural network to segment the sperm head, so as to obtain sperm targets from the background of the sperm image, and then used the SUPPORT vector machine SVM to classify the segmented head pixels.…”

Section: Related Workmentioning

confidence: 99%

A method for sperm activity analysis based on feature point detection network in deep learning

Chen¹,

Yang²,

Chen³

et al. 2022

Front. Comput. Sci.

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Sperm motility is an important index to evaluate semen quality. Computer-assisted sperm analysis (CASA) is based on the sperm image, through the image-processing algorithm to detect the position of the sperm target and track tracking, so as to judge the sperm activity. Because of the small and dense sperm targets in sperm images, traditional image-processing algorithms take a long time to detect sperm targets, while target-detection algorithms based on the deep learning have a lot of missed detection problems in the process of sperm target detection. In order to accurately and efficiently analyze sperm activity in the sperm image sequence, this article proposes a sperm activity analysis method based on the deep learning. First, the sperm position is detected through the deep learning feature point detection network based on the improved SuperPoint, then the multi-sperm target tracking is carried out through SORT and the sperm motion trajectory is drawn, and at last the sperm survival is judged through the sperm trajectory to realize the analysis of sperm activity. The experimental results show that this method can effectively analyze the sperm activity in the sperm image sequence. At the same time, the average detection speed of the sperm target detection method in the detection process is 65fps, and the detection accuracy is 92%.

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Section: Related Workmentioning

confidence: 99%

A method for sperm activity analysis based on feature point detection network in deep learning

Chen¹,

Yang²,

Chen³

et al. 2022

Front. Comput. Sci.

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“…In literature, several studies were conducted to investigate the effects of spatial data augmentation techniques in the classification of sperm morphology datasets (Ilhan et al, 2020;Tortumlu & Ilhan, 2020;Yüzkat et al, 2020). Ilhan et al performed the spatial augmentation techniques on the SMIDS dataset in order to increase the classification performance of three deep networks.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, some networks have resulted in lower accuracy after a point of augmentation scale because of feeding the almost similar images to networks. This is the limitation of the spatial-based augmentation techniques, which causes the non-informative training phase, as demonstrated in (Yüzkat et al, 2020). In another study, the effects of spatial augmentation techniques have been explored for mobile-based networks (Tortumlu & Ilhan, 2020).…”

Section: Related Workmentioning

confidence: 99%

The Implementation of DCGAN in the Data Augmentation for the Sperm Morphology Datasets

Balayev¹,

Guluzade²,

Aygün³

et al. 2021

European Journal of Science and Technology

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A large amount of data is the key requirement in order to train a neural network efficiently. Using a small size training set in network training causes low accuracy for model performance over the testing set and also hard to implement the model in practice. Similar to many other problems, sperm morphology datasets are also limited for training the neural network-based deep networks in order to provide an automatic evaluation of sperm morphometry. Data augmentation mitigates this problem by utilizing actual data more effectively. The standard data augmentation techniques focus on only spatial changes over the images and can only produce a restricted number of useful informative and disjunctive data. Therefore, in order to create more distinctive and diverse data than the regular spatial domain-based augmentation techniques, a deep learning-based data augmentation technique which is known as the generative model, is trained in this study for the sperm morphology datasets. The deep convolutional generative adversarial network (DCGAN) was optimized and utilized in this study for three well-known sperm morphometry datasets as SMIDS, HuSHeM, and SCIAN-Morpho. Each dataset was individually augmented to a 1000 sample size by the proposed approach. In order to optimize the network with different parameters and observe the generated data, a graphical user interface has been designed. For the similarity evaluation of the generated images to original images, the Fréchet Inception Distance (FID) score was utilized. The FID results indicate that the most similar generated images have been obtained for SMIDS with an average of 29.06 FID score. The worst performance (Average FID = 53.46) was obtained for the SCIAN-Morpho dataset, which has low resolution and data imbalance problems. Lastly, DCGAN based proposed approach resulted in an average of 44.25 FID score for the HuSHeM dataset.

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“…Yüzkat et al applied various interpolation and data augmentation methods to increase the classification performance of the SCIAN-MorphoSpermGS data set. Then, 62% classification accuracy was achieved by using a modified VGG-19 network (Yüzkat et al, 2020). In a more recent study, they presented a fully automated analyzing approach for classifying sperm images.…”

Section: Introductionmentioning

confidence: 99%

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

Yuzkat¹,

İlhan²,

Aydın³

2021

European Journal of Science and Technology

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Infertility is defined by the World Health Organization as the inability of a woman to become pregnant even though the couple had sexual intercourse for one year without any protection. Male and/or female factors might be the reasons for infertility. When diagnosing the male factors, sperm specimens are analyzed in a laboratory environment under certain conditions. The morphological abnormality, characteristic motility and concentration of sperm are examined in the analysis called spermiogram. Spermiogram tests can be done manually by doctors, as well as by using computer-assisted sperm analyzing systems. The importance of computer aided analysis is increasing day by day because visual inspection can give different results from person to person and is costly. In this study, the effect of different color spaces as a preprocessing step is investigated to increase the classification performance of a computer based analyzing approach for sperm morphology. Three sperm morphology data sets abbreviated as SMIDS, HuSHeM and SCIAN-Morpho were used in the experimental tests. Data augmentation was applied on the data sets due to the unbalanced distribution of sperm images among the classes and insufficient data. Then, data sets were converted to two well-known color spaces, LAB and HSV to observe the effects of color space in the classification. MobileNetV2 was utilized as the classification model. In order to indicate the effects of color spaces, results were compared with previously published study where no color transform was implemented. The classification of images in LAB and HSV color spaces had better results than RGB images trained under the same conditions. The maximum classification accuracies of 89%, 85% and 68% were obtained for SMIDS, HuSHeM, SCIAN-Morpho data sets by using the color space transform idea, respectively.

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Morphological Classification of Low Quality Sperm Images Using Deep Learning Networks

Cited by 6 publications

References 7 publications

A method for sperm activity analysis based on feature point detection network in deep learning

A method for sperm activity analysis based on feature point detection network in deep learning

The Implementation of DCGAN in the Data Augmentation for the Sperm Morphology Datasets

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

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