The Analysis of Mobile Platform based CNN Networks in the Classification of Sperm Morphology

Tortumlu, Omer Lutfu; İlhan, Hamza Osman

doi:10.1109/tiptekno50054.2020.9299281

Cited by 6 publications

(9 citation statements)

References 9 publications

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“…In the previously published study (Tortumlu and Ilhan, 2020), mobile platform-based networks, MobileNetV1 and MobileNetV2, were applied to three original formats of data sets. MobileNetV2 was defined as the most successful network according to the obtained results.…”

Section: Methodsmentioning

confidence: 99%

“…In order to investigate the mobile platform based pre-trained network performances for the classification of sperm images, Tortumlu and Ilhan, compared the classification performances of MobileNet V1 and V2 over three sperm morphology data sets (Tortumlu and Ilhan, 2020). They achieved 77%, 88% and 67% classification accuracies for HuSHeM, SMIDS and SCIAN-Morpho GS datasets, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we aimed to increase the classification performance of the mobile platform based network, MobileNetV2. In the previously published study (Tortumlu and Ilhan, 2020), classification was made using RGB formatted sperm images. In the presented study, we investigated the effect of different color spaces in which the morphology of sperm images appear more distinctive in the classification.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…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%

“…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). They tested over three sperm morphology datasets and reported that the augmentation approach increased the performance for all datasets.…”

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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Artificial Intelligence: Revolution in Assisted Reproductive Technology

Barkavi,

Yamuna,

Jayaram

2023

Algorithms for Intelligent Systems

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The Analysis of Mobile Platform based CNN Networks in the Classification of Sperm Morphology

Cited by 6 publications

References 9 publications

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

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

Artificial Intelligence: Revolution in Assisted Reproductive Technology

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