Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...
Introduction: Accurate segmentation of pelvic fractures from computed tomography (CT) is crucial for trauma diagnosis and image-guided reduction surgery. The traditional manual slice-by-slice ...
U-NET, a convolutional neural network architecture, demonstrates exceptional performance in brain tumour segmentation, achieving 98.56% accuracy and 99% F-score in MRI image analysis. Brain health is ...
Abstract: Deep learning models exhibit promising potential in multimodal remote sensing image semantic segmentation (MRSISS). However, the constrained access to labeled samples for training deep ...
NEW CUTTING-edge deep-learning model using lung CT imaging has shown significant potential for accurately identifying and segmenting lung tumours, according to researchers based in the USA. The ...
Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating ...
Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed ...
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