Automated sperm morphology analysis approach using a directional masking technique

İlhan, Hamza Osman; Serbes, Görkem; Aydın, Nizamettin

doi:10.1016/j.compbiomed.2020.103845

Cited by 23 publications

(7 citation statements)

References 72 publications

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“…They increased their previous success rate for SMIDS from 84% to 86%. For HuSHeM, they achieved an accuracy of 85% (Ilhan et al, 2020a). Ilhan et al used MobileNetV2 in another study similar to this study.…”

Section: Introductionsupporting

confidence: 69%

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: Introductionsupporting

confidence: 69%

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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“…One of these studies applies multi-stage cascade-connected preprocessing and machine learning based on a non-linear support vector machine (SVM) kernel. The results of this study provide an accuracy of 86.6% for the human sperm head morphology (HuSHeM) dataset and 85.7% for the Sperm Morphology image data set (SMIDS), respectively [3]. Another study developed an artificial intelligence algorithm using a network-based deep transfer learning approach and deep multi-task transfer learning (DMTL), to classify Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  sperm heads, vacuoles, and acrosomes as normal or abnormal.…”

Section: Introductionmentioning

confidence: 88%

Predicting the value of sperm analysis using an electronic nose

Wijaya

Kusumaatmaja

Rizal

2022

IJEECS

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Total motile sperm count and DNA fragmentation Index are two parameters in sperm analysis that have recently been used to determine the outcome of the management of cases of male infertility. Total Motile Sperm Count is one of the values considered better than the 2010 World Health Organization standard sperm analysis in terms of predictive value for the success of the spontaneous ongoing pregnancy rate. High DNA Fragmentation Index values were associated with lower pregnancy success and an increased risk of low fertilization rate or total fertilization failure. In this study, we developed a method to classify sperm analysis based on total motility sperm count and DNA Fragmentation Index values by using an electronic nose. In the total motility sperm count (TMSC) study, we use four algorithms with the result of accuracy values 95% and in the DNA fragmentation Index study, we get a fairly good accuracy value for two algorithms with the accuracy values 70%.

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“…The morphological gradient corresponds to the subtraction between dilation and erosion operations and generates an image in which the values of each pixel indicate the contrast intensity with respect to its neighbors. The morphological gradient is useful in edge detection and segmentation processes since the output image refers to the contour or silhouette of the image (31) . The structural element used is of rectangular type with a matrix of size 3x3 (the nine positions of the matrix will take a value of 1).…”

Section: Morphological Gradient For Edge Detectionmentioning

confidence: 99%

approach to edge detection in medical imaging through histogram analysis and morphological gradient

Rondón¹,

Carvajal

Casadiego³

et al. 2022

inycomp

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Edge detection takes importance in image processing systems for computer-aided diagnosis, wheresharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions ofinterest to the specialist. A method for feature enhancement and edge detection in medical imageswas developed using image processing by analyzing the pixel distribution histogram andmorphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT datasetwere used. The method is based on image processing and is applied to mammography and chest CTimages, where blur filtering steps are accompanied by morphological gradient filtering, in addition toobtaining the threshold for edge detection by analyzing the point of maximum pixel concentrationaccording to the distribution histogram. The processing is presented in a graphical user interfacedeveloped in Python language. The method is validated by comparison with other edge detectiontechniques such as the Canny Algorithm, and with deep learning methods such as Holistically-NestedEdge Detection. The proposed method improves image quality in both mammograms and CT scanscompared to other techniques. It also presents the best performance considering internal and externaledge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aideddiagnosis processes due to its high efficiency in edge detection.

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Automated sperm morphology analysis approach using a directional masking technique

Cited by 23 publications

References 72 publications

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

Effects of Color Space Transformations on Classification Performance of Sperm Morphology

Predicting the value of sperm analysis using an electronic nose

approach to edge detection in medical imaging through histogram analysis and morphological gradient

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