Sperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total of 49345 RGB sperm morphology patches were obtained using the proposed image acquisition technique and three different Diff-Quick staining methods: BesLab, Histoplus, and GBL. The images were labeled by experts under 18 classes, including sperm head, neck, and tail abnormality types, along with a normal class. The head category includes amorphous, tapered, double, pyriform, pin, vacuolated, narrow acrosome, and round. The neck category encompasses thin, thick, twisted, and asymmetrical. The tail category includes double, curly, long, short, and twisted. The Efficient-V2-Medium achieved accuracy rates of 65.05% and 67.42% on the BesLab and Histoplus datasets, respectively, while the GBL dataset yielded an accuracy of 63.58% using the Efficient-V2-Small. This study experimentally demonstrates that the Histoplus staining method is more suitable for deep learning-based automated analysis systems. As a reference for future studies, 35 different deep learning architectures were trained on the proposed dataset, establishing a classification baseline. The results show that the dataset can be successfully applied to complex deep learning models. Additionally, it addresses the absence of a large-scale sperm morpholgy analysis public datasets and can serve as a standard benchmark for future studies.

Gerçek zamanlı çalışan sistemlerde görüntü işleme uygulamaları yapmak son zamanlarda oldukça popüler olan bir konu haline gelmiştir. Yapay zekâ alanının alt dallarından biri olan derin öğrenme yöntemleri ve görüntülerden nesne tespiti yapma alanında kullanılan görüntü işleme algoritmaları birlikte kullanılarak, otonom otomobiller, otonom insansız hava araçları, yardımcı robot teknolojileri, engelli ve yaşlı bireyler için asistan teknolojileri gibi birçok alanda uygulamalar geliştirilmektedir. Yapılan çalışmada, görme engelli bireyler, otonom araçlar ve robotlar tarafından kullanılabilecek yardımcı bir teknoloji sistemi tasarlamak için dokunsal parke yüzeylerinin derin öğrenme yöntemleriyle tespit edilmesi gerçekleştirilmiştir. Geleneksel görüntü işleme algoritmalarının aksine bu çalışmada derin öğrenme yöntemleri ile görüntü işleme algoritmaları birlikte kullanılmıştır. Nesne tespit etme yöntemleri içinde en iyi yöntemlerden biri olan You Only Look Once-V3(YOLO-V3) modeli DenseNet modeli ile birleştirilerek YOLOV3-Dense modeli oluşturulmuştur. YOLO-V2, YOLO-V3 ve YOLOV3Dense modelleri tarafımızca oluşturulmuş olan ve içerisinde 4580 etiketli görsel bulunan Marmara Dokunsal Parke Yüzeyi(MDPY) veri seti üzerinde ayrı ayrı eğitildikten sonra performansları test veri seti üzerinde birbirleri ile karşılaştırılmıştır. %89 F1-skor, %92 ortalama hassasiyet ve %81 IoU değerleri ile YOLOV3-Dense modelinin dokunsal parke yüzeyi tespit etmede diğer modellerden daha iyi olduğu gözlemlenmiştir. Saniyede 60 kare çalışma hızı ile YOLOV3-Dense modeli gerçek zamanlı çalışan sistemlerde de kullanılabilmektedir.

Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology—referring to the shape, size, and structural integrity of sperm cells—is a key indicator in diagnosing male infertility and selecting viable sperm in assisted reproductive technologies such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI). However, traditional manual evaluation methods are highly subjective and inconsistent, creating a need for standardized, automated systems. Objectives: This study aims to develop a robust and fully automated sperm morphology classification framework capable of accurately identifying a wide range of morphological abnormalities, thereby minimizing observer variability and improving diagnostic support in reproductive healthcare. Methods: We propose a novel ensemble-based classification approach that combines convolutional neural network (CNN)-derived features using both feature-level and decision-level fusion techniques. Features extracted from multiple EfficientNetV2 variants are fused and classified using Support Vector Machines (SVM), Random Forest (RF), and Multi-Layer Perceptron with Attention (MLP-Attention). Decision-level fusion is achieved via soft voting to enhance robustness and accuracy. Results: The proposed ensemble framework was evaluated using the Hi-LabSpermMorpho dataset, which contains 18 distinct sperm morphology classes. The fusion-based model achieved an accuracy of 67.70%, significantly outperforming individual classifiers. The integration of multiple CNN architectures and ensemble techniques effectively mitigated class imbalance and enhanced the generalizability of the model. Conclusions: The presented methodology demonstrates a substantial improvement over traditional and single-model approaches in automated sperm morphology classification. By leveraging ensemble learning and multi-level fusion, the model provides a reliable and scalable solution for clinical decision-making in male fertility assessment.

Introduction: Sperm morphology is a fundamental parameter in the evaluation of male infertility, offering critical insights into reproductive health. However, traditional manual assessments under microscopy are limited by operator dependency and subjective interpretation caused by biological variation. To overcome these limitations, there is a need for accurate and fully automated classification systems. Objectives: This study aims to develop a two-stage, fully automated sperm morphology classification framework that can accurately identify a wide spectrum of abnormalities. The framework is designed to reduce subjectivity, minimize misclassification between visually similar categories, and provide more reliable diagnostic support in reproductive healthcare. Methods: A novel two-stage deep learning-based framework is proposed utilizing images from three staining-specific versions of a comprehensive 18-class dataset. In the first stage, sperm images are categorized into two principal groups: (1) head and neck region abnormalities, and (2) normal morphology together with tail-related abnormalities. In the second stage, a customized ensemble model—integrating four distinct deep learning architectures, including DeepMind’s NFNet-F4 and vision transformer (ViT) variants—is employed for detailed abnormality classification. Unlike conventional majority voting, a structured multi-stage voting strategy is introduced to enhance decision reliability. Results: The proposed framework consistently outperforms single-model baselines, achieving accuracies of 69.43%, 71.34%, and 68.41% across the three staining protocols. These results correspond to a statistically significant 4.38% improvement over prior approaches in the literature. Moreover, the two-stage system substantially reduces misclassification among visually similar categories, demonstrating enhanced ability to detect subtle morphological variations. Conclusions: The proposed two-stage, ensemble-based framework provides a robust and accurate solution for automated sperm morphology classification. By combining hierarchical classification with structured decision fusion, the method advances beyond traditional and single-model approaches, offering a reliable and scalable tool for clinical decision-making in male fertility assessment.

Traditional sperm morphology assessment relies on manual visual inspection or semi-automated computer-aided sperm analysis (CASA) systems, which often require labor-intensive pre-processing steps. While recent machine learning approaches, particularly convolutional neural networks (CNNs), have improved feature extraction from sperm images, achieving a fully automated and highly accurate system remains challenging due to the complexity of sperm morphology and the need for specialized image adjustments. This study presents a novel, end-to-end automated sperm morphology analysis framework based on vision transformers (ViTs), which processes raw sperm images from two benchmark datasets-Human Sperm Head Morphology (HuSHeM) and Sperm Morphology Image Data Set (SMIDS)-without manual pre-processing. We conducted an extensive hyperparameter optimization study across eight ViT variants, evaluating learning rates, optimization algorithms, and data augmentation scales. Our experiments demonstrated that data augmentation significantly enhances ViT performance by improving generalization, particularly in limited-data scenarios. A comparative analysis of CNNs, hybrid models, and pure ViTs revealed that transformer-based architectures consistently outperform traditional methods. The BEiT_Base model achieved state-of-the-art accuracies of 92.5% (SMIDS) and 93.52% (HuSHeM), surpassing prior CNN-based approaches by 1.63% and 1.42%, respectively. Statistical significance (p < 0.05, t-test) confirmed these improvements. Visualization techniques (Attention Maps, Grad-CAM) further validated ViTs’ superior ability to capture long-range spatial dependencies and discriminative morphological features, such as head shape and tail integrity. Our work bridges a critical gap in reproductive medicine by delivering a scalable, fully automated solution that eliminates manual intervention while improving diagnostic accuracy. These findings underscore the potential of transformer-based models in clinical andrology, with implications for broader applications in biomedical image analysis.