Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
Abstract: Improving the resolution of medical images is an important task in ensuring trustworthy diagnosis and effective monitoring of diseases. Of the newest deep learning algorithms, Convolutional ...
Abstract: Intracranial hemorrhage (ICH) refers to bleeding within the brain, a global concern that underscores the im-portance of early detection. ICH is typically detected using computed tomography ...
Abstract: Accurate real-time fault detection, localization, and classification techniques are necessary to maintain grid stability and prevent faults. Traditional techniques have low accuracy rates, ...
Abstract: Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning ...
Abstract: Fresh storage of fruits and vegetables is crucial for minimising waste, consumer health, and quality of food. Produce freshness classification can greatly improve supply chain effectiveness ...
Abstract: Hyperspectral image classification (HSIC) is a valuable method for identifying coastal wetland vegetation, but challenges like environmental complexity and difficulty in distinguishing land ...
Abstract: Leaf diseases are a major challenge for agricultural productivity, requiring accurate and efficient detection methods. This research presents an effective method for multi-class ...
Abstract: Using dermoscopic images for the classification of skin lesion is crucial for early skin cancer detection, but resource limitations hinder complex deep learning model applications in ...
Abstract: Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively ...
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