RETRACTED ARTICLE: An improved convolutional neural network for abnormality detection and segmentation from human sperm images

Prabaharan, L.; Raghunathan, A. V.

doi:10.1007/s12652-020-02773-7

Cited by 13 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Miahi et al improved an architecture for vacuole abnormality recognition with an accuracy of 91.66% [7]. Prabaharan et al presented a CNN network for the detection of abnormal sperms with abnormal dimensions with 98.99% of accuracy and approved the efficiency of the program [8]. P., Zuhdi et al [9] introduced DeepSperm, a profound brain network that utilizes a particular discovery layer to identify little items.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Sperms using ResNet-50 Deep Neural Network

Mashaal¹,

Eldosoky²,

Mahdy³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Infertility is a disease which scientists around the world are concerned with. The disease of infertility also is a worldwide health concern of many people in the community. The andrologists are continually searching for further developed techniques for any related problems. The intracytoplasmic sperm injection (ICSI) method is a widely recognized strategy for accomplishing pregnancy and considered as one of the best methods for infertility treatment worldwide. Choosing the best sperms are done using the vision through the specimen which is reliant on the abilities and the cleverness of the embryologists and as such inclined to human errors. Subsequently, a system that detects the normal sperms automatically is required for speedy and more precise outcomes. Deep learning approaches are usually effective for classification and detection purposes. This paper uses the Residential Energy Services Network (ResNet-50) deep learning architecture to recognize human sperms after classification of human sperm heads. The ResNet-50 proposed model achieved an accuracy of 96.66%. This proposed model demonstrated its efficiency at the detection of healthy sperms. The healthy sperms are used for the injection into eggs by the andrologists who always look for easier and more advanced methods in order to increase the success rate of ICSI process.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Sperms using ResNet-50 Deep Neural Network

Mashaal¹,

Eldosoky²,

Mahdy³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Several studies have conducted research on sperm analysis, both in the form of morphological analysis and motility analysis of sperm movement, one of which is research conducted by Prabaharan and Raghunathan (2021), which uses a machine learning approach, especially deep convolutional neural networks (CNN), to perform classification, detection, and segmentation processes. This method also utilizes morphological approaches to represent images [35]. In the proposed method, deep convolutional neural networks are used to detect infertility disorders in men.…”

Section: Introductionmentioning

confidence: 99%

“…In the proposed method, deep convolutional neural networks are used to detect infertility disorders in men. Image morphology process is applied using an improved Otsu threshold method for sperm image segmentation, which helps to detect abnormal regions using convolutional layers [35]. The database is sourced from a human sperm image analysis dataset.…”

Section: Introductionmentioning

confidence: 99%

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Mas Diyasa,

Saputra,

Gunawan

et al. 2024

Journal of Robotics and Control

View full text Add to dashboard Cite

Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

show abstract

“…Yet, only a handful of algorithms use deep learning to detect sperm cells. Prabaharan et al [ 25 ] used image morphological processing to reduce noise, segmented sperm images by the E-Ostu threshold method, and then used a convolutional neural network to detect abnormal regions. The method achieved 98.99% accuracy and was able to effectively detect abnormal sperm morphology.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

et al. 2023

View full text Add to dashboard Cite

Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

show abstract

RETRACTED ARTICLE: An improved convolutional neural network for abnormality detection and segmentation from human sperm images

Cited by 13 publications

References 28 publications

Classification of Human Sperms using ResNet-50 Deep Neural Network

Classification of Human Sperms using ResNet-50 Deep Neural Network

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

Contact Info

Product

Resources

About