Content-based image retrieval (CBIR) systems have formidable obstacles in connecting human comprehension with machine-driven feature extraction due to the exponential expansion of visual data across many areas. Robust performance across varied datase...
Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach...
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, comp...
Delineation of multiple organs in murine µCT images is crucial for preclinical studies but requires manual volumetric segmentation, a tedious and time-consuming process prone to inter-observer variability. Automatic deep learning-based segmentation c...
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...
Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. Existing multimodal fusion models usually perform intra-modal fusion at both shal...
Deep learning primarily operates on images which contain hidden patterns that are quantified through pixel intensities. Deep learning is used to analyze the image patterns and to recognize the objects. The detection process includes the creation of l...
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...
Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care. Deep convolutional neural networks trained on MRI data can enhance diagnostic precisi...
The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatment. Although Transformer-based models are effective at capturing global context, they are often ineff...
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