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Deep Learning: Basics and Convolutional Neural Networks (CNNs) [PDF]
, 2023 AbstractDeep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components.
Vakalopoulou M +4 more
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Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics. [PDF]
Mol Divers, 2021 AbstractConvolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods.
Vaz JM, Balaji S.
europepmc +4 more sources
Training convolutional neural networks with the Forward–Forward Algorithm [PDF]
Scientific ReportsRecent successes in image analysis with deep neural networks are achieved almost exclusively with Convolutional Neural Networks (CNNs), typically trained using the backpropagation (BP) algorithm.
Riccardo Scodellaro +3 more
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DK-CNNs: Dynamic kernel convolutional neural networks [PDF]
Neurocomputing, 2021 Abstract This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension of kernel weights. The proposed DK-CNN applies regular convolution to the DK weights, which rely on a latent variable, and discretizes the space of the ...
Liu, Jialin +4 more
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Convolutional Neural Networks using FPGA-based Pipelining
Iraqi Journal for Computer Science and Mathematics, 2023 In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use of FPGA-based pipelining for hardware acceleration of CNNs.
Gheni A. Ali, ahmed hussein ali
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Computation, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
Mohammad Mustafa Taye
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The predictive skill of convolutional neural networks models for disease forecasting.
PLoS ONE, 2021 In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza ...
Kookjin Lee, Jaideep Ray, Cosmin Safta
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A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
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Journal of Education, Health and Sport, 2021 Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka +4 more
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Egyptian Journal of Remote Sensing and Space Sciences, 2022 Although various deep neural networks such as convolutional neural networks (CNNs) have been suggested for classification of polarimetric synthetic aperture radar (PolSAR) images, but, they have several deficiencies.
Maryam Imani
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