Abstract: Correct weather condition classification is crucial in intelligent applications such as autonomous cars, smart surveillance, and environmental monitoring. In this paper, a hybrid deep ...

The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, China 2 College of Geophysics, Chengdu University of Technology, Chengdu, China ...

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Abstract: Prostate cancer diagnosis has been transformed by the integration of machine learning techniques, particularly deep learning models. This study focuses on utilizing ResNet for Gleason score ...

This Collection calls for submissions of original research into techniques that facilitate the advancement of deep learning for image analysis and object detection, driving computer vision forward and ...

Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. Methods: We retrospectively ...