Results 181 to 190 of about 390,241 (222)

Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach. [PDF]

Tomography
Das D, Sarkar C, Das B.
europepmc   +1 more source

Non-Invasive Localization of Epileptogenic Zone in Drug-Resistant Epilepsy Based on Time-Frequency Analysis and VGG Convolutional Neural Network. [PDF]

Bioengineering (Basel)
Liu Y, Wang Y, Wang T.
europepmc   +1 more source

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.

JMIR Med Inform
Lei C, Jiang Y, Xu K, Liu S, Cao H, Wang C.   +5 more
europepmc   +1 more source

CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer. [PDF]

Bioengineering (Basel)
Khan A, Sajid MZ, Khan NA, Youssef A, Abbas Q.   +4 more
europepmc   +1 more source

Convolutional Neural Networks [PDF]

, 2017
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
Ragav Venkatesan, Baoxin Li
  +8 more sources

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Convolutional Neural Network

2021
Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data.
Y. V. R. Nagapawan, Kolla Bhanu Prakash, G. R. Kanagachidambaresan   +2 more
  +6 more sources

Convolutional neural networks

2021
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L., Sebastianelli A., Rosso M. P. D., Spiller D., Puglisi E., Nowakowski A., Bernardi M. L., Cimitile M.   +7 more
openaire   +4 more sources

Convolutional Neural Networks

2023
Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können. Insbesondere eignen sich CNNs für das End-to-End-Lernen auf Bildern oder ähnlich strukturierten Daten. Dabei können CNNs Merkmale von Bildern anhand der Pixelwerte effizient lernen und beispielsweise sehr gute ...
Rudolph, Yannick, Brefeld, Ulf
openaire   +2 more sources