A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods

İlhan, Hamza Osman; Sigirci, Ibrahim Onur; Serbes, Görkem; Aydın, Nizamettin

doi:10.1007/s11517-019-02101-y

Cited by 45 publications

(18 citation statements)

References 59 publications

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“…Our staining‐free morphology measurement resulted in errors less than 5%. Previous methods require invasive staining to measure sperm morphology, and their measurement errors ranged from 5% to 15% compared with manual benchmarking 8,25 …”

Section: Resultsmentioning

confidence: 99%

Automated motility and morphology measurement of live spermatozoa

et al. 2021

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Background: Automated sperm analysis has wide applications in infertility diagnosis.Existing systems are not able to measure sperm count and both motility and morphology of individual live spermatozoa. Morphology measurement requires invasive staining, making the spermatozoa after morphology measurement not applicable to infertility treatment.Objective: To evaluate the reproducibility and reliability of automated measurement of individual live sperm's motility and morphology.Materials and methods: Fresh semen samples were obtained from twenty male partners attending for fertility investigations. The system firstly measured motility for all the spermatozoa within the field of view under a low magnification (20×), then a spermatozoa of interest is selected by the user and automatically relocated by the system after switching to a high magnification (100×) for morphology measurement.Reproducibility of sperm measurements was evaluated by intraclass correlation coefficients on consecutive measurement. Reliability of motility and morphology measurement was evaluated by tracking error rate and limits of agreement, respectively, with manual measurement as benchmark.Results: Measurement of all motility and morphology parameters had intraclass correlation coefficients higher than 0.94. Sperm motility measurement had a tracking error rate of 2.1%. Limit of agreement analysis indicated that automated measurement and manual measurement of sperm morphology were interchangeable. Automated measurement of all morphology parameters was not statistically different from manual measurement, as confirmed by the paired sample t test.Discussion: Automated motility and morphology measurement of single sperm revealed high reproducibility and reliability. The system also achieved a high efficiency for motility and morphology measurement. In addition to the intracytoplasmic sperm injection (ICSI) samples with polyvinylpyrrolidone (PVP), the developed sperm measurement technique is also effective for analyzing semen and washed samples. The system provides a valuable tool for quantitative measurement and selection of single spermatozoa for ICSI. It can also be used for sperm motility and morphology analysis in andrology laboratories.

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Section: Resultsmentioning

confidence: 99%

Automated motility and morphology measurement of live spermatozoa

et al. 2021

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“…In this paper, we have benchmarked the FGrade dataset by applying various existing classical methods and deep learning models. We have used state-ofthe-art deep models such as ResNet50 [30], ResNet101 [31], ResNet152 [32], VGG16 [33], VGG19 [34], NASNetMobile [35], NasNetLarge [35], InceptionV3 [36], MobileNet [37], DenseNet121 [38], DenseNet169 [38], and DenseNet201 [38].…”

Section: Benchmarking Methods and Discussionmentioning

confidence: 99%

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

Das

Kar

Sekh

2021

Communications in Computer and Information Science

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Quality-based grouping or grading of fruits and vegetables played an important role in many stages of agricultural production. The method is used for sorting items based on the size, color, presence of damage etc. Manual grading is laborious and time consuming. Hence, not suitable for processing massive amounts. Grading different fruits and vegetables based on size or damages is popular in literature. More complex problems such as freshness based grading got less attention.Here, we introduce a new freshness grading dataset namely FGrade. The dataset is a collection of (∼6K) high-quality tomato images collected in a form of day-by-day degradation. The dataset is labelled in 2, 4, 6 and 10 classes using a structured manner. We have benchmarked the dataset using state-of-the-art image classification methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly (https://github.com/skarifahmed/FGrade).

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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%

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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A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods

Cited by 45 publications

References 59 publications

Automated motility and morphology measurement of live spermatozoa

Automated motility and morphology measurement of live spermatozoa

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

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

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