Automatic Healthy Sperm Head Detection using Deep Learning

Mashaal, Ahmad Abdelaziz; Eldosoky, Mohamed A. A.; Mahdy, Lamia Nabil; Ezzat, Kadry Ali

doi:10.14569/ijacsa.2022.0130486

Cited by 4 publications

(2 citation statements)

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“…Aristoteles et al used deep learning approaches for healthy sperms detection using video tracking using deep learning with result 90.31% average precision sperms and the system detects the non-sperm parts automatically rather than the manual methods that require more time and high costs [14]. Ahmad Mashaal et al proposed a system for the recognition of healthy sperms using VGG16 deep learning model and used Otsu's thresholding method for the segmentation of sperm head and other methods for the enhancement of images with accuracy reached 97.92% [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Sperms using ResNet-50 Deep Neural Network

Mashaal¹,

Eldosoky²,

Mahdy³

et al. 2023

IJACSA

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Sperms using ResNet-50 Deep Neural Network

Mashaal¹,

Eldosoky²,

Mahdy³

et al. 2023

IJACSA

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“…Translating a technique from technical development to clinical deployment can involve various factors that impact the reproducibility of the developed technique. Regardless of applications or the types of cells to analyze, the first technical step for deep learning models is often to visually identify and locate an object (oocyte [33,34], sperm [35][36][37][38][39], and embryo [20,[40][41][42]) in images. Different clinics, however, use different image acquisition conditions (e.g., microscope brands and models, imaging modes [43][44][45], magnifications [9,33], illumination intensity, and camera resolutions [13][14][15]39] etc.…”

Section: Introductionmentioning

confidence: 99%

Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study

Wang,

Jin,

Jiang

et al. 2024

Preprint

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Background: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the reproducibility of object detection models, using sperm analysis as a pilot example. Methods: Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model reproducibility. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. Results: Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. Conclusions: The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model reproducibility. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced reproducibility across clinics.

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