Infertility is a global issue, and approximately 50% of cases are due to male factors, with defective spermatogenesis being the main one. For studies of spermatogenesis, evaluating the seminiferous tubule stage is essential. However, current evaluati...
Classifying bird species is essential for ecological study and biodiversity protection, currently, conventional approaches are frequently laborious and susceptible to mistakes. Convolutional Neural Networks (CNNs) provide a more reliable option for f...
Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To addr...
Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs)...
Parkinson's Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very challenging. However, using patterns like tremors for early diagnosis, handwriting analysis has becom...
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...
Stress is widely acknowledged as a significant contributor to health issues. Recognizing stress involves assessing an individual's physiological and psychological responses to stressors, which is crucial for human well-being. Physiological signal-bas...
Brain image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis and treatment planning. Federated Learning (FL) enables collaborative model training across institutions while safeguarding sensitive patient data. The i...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 30, 2025
Brain regional segmentation is an image-processing approach widely used in brain image analyses. Deep learning models that perform segmentation alone play an important role in medical fields such as automatic diagnosis and prognosis prediction. This ...
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