An Analysis of Convolutional Neural Networks
DOI:
https://doi.org/10.55524/Keywords:
Convolutional Neural Networks, Deep Learning, Networks, Radiology, SupervisedAbstract
Convolutional neural networks (CNNs), form of artificial neural network (ANN) prominent in computer vision, are finding traction in diversity of sectors, comprising radiology. CNN employs a variety of building pieces, including as convolution, pooling layers, & fully linked layers, for acquiring spatial data hierarchy autonomously & adaptively via backpropagation. This review paper investigates core concepts of CNN & how se are used to numerous radiological jobs, as well as issues & future prospects in radiology. In addition, this work will explore two issues that arise when using CNN to radiological tasks: restricted datasets & overfitting, as well as approaches for mitigating m. Conceptual underst&ing, advantages, & limitations of CNN is crucial for realising its full potential in diagnostic radiology & improving radiologists' performance & patient care.
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