Sperm morphology analysis by using the fusion of two-stage fine-tuned deep networks

İlhan, Hamza Osman; Serbes, Görkem

doi:10.1016/j.bspc.2021.103246

Cited by 11 publications

(13 citation statements)

References 30 publications

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“…Table 1 highlights the different properties across the three datasets. Another difference distinguishing the SVIA, HuSHem, and SMIDS is the RGB color space on the latter two datasets [ 13 , 21 ]. Lastly, there are differences in image size between the datasets.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast, the SCIAN [ 20 ] dataset has 1854 sperm head images. A third sperm morphology dataset, SMIDS, compares three classes with a total of 3000 images recently available [ 21 ]. Previous research has mostly used convolutional neural network (CNN) [ 17 , 18 , 19 ], dictionary learning [ 13 ], or machine learning (ML) [ 20 ] models for classification.…”

Section: Introductionmentioning

confidence: 99%

“…It also achieved 95.7% accuracy. Using a late (decision level) fusion architecture, a study by [ 21 ] achieved 90.87% accuracy on the SMIDS dataset. This particular research also investigated the model’s capability to replace rotation and cropping human intervention for automation purposes.…”

Section: Introductionmentioning

confidence: 99%

“…The trained models were evaluated using a three-fold and five-fold cross-validation technique on several performance metrics, namely F1-score and accuracy. The proposed models were also tested on other sperm morphology datasets, such as the HuSHem [ 13 ] and SMIDS [ 21 ], to assess their generalization ability. Comparison to the state-of-the-art models was based on F1-score and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm

Mahali,

Leu,

Darmawan

et al. 2023

Sensors

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Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets’ different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.’s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm

Mahali,

Leu,

Darmawan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Color, size and texture information were not effective in sperm detection and are basically ineffective in sperm detection based on color, size and texture information. On the other hand, sperm cells have highly similar morphology, which greatly reduces the feasibility of detection algorithms to extract information from the morphology [ 10 , 11 , 12 ]. At the same time, semen samples are doped with a lot of epithelial cells or other impurities, which put forward higher requirements for accurate detection of sperm in video frames.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Potential for artificial intelligence in medicine and its application to male infertility

Kobayashi

2024

Reprod Medicine & Biology

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BackgroundThe third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.MethodsThis review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.Main Findings (Results)In research on male infertility, I established an AI‐based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non‐obstructive azoospermia, both by no‐code AI.ConclusionsAI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No‐code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.

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Sperm morphology analysis by using the fusion of two-stage fine-tuned deep networks

Cited by 11 publications

References 30 publications

A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm

A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm

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

Potential for artificial intelligence in medicine and its application to male infertility

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