An Analysis of Convolutional Neural Networks

Authors

  • Indu Sharma Assistant Professor, Department of Computer Applications, RIMT University, Mandi Gobindgarh, Punjab, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Convolutional Neural Networks, Deep Learning, Networks, Radiology, Supervised

Abstract

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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Published

2022-03-30

Issue

Vol. 10 No. 2 (2022): International Journal of Innovative Research in Computer Science and Technology (Mar ) 2022

Section

Articles

How to Cite

An Analysis of Convolutional Neural Networks . (2022). International Journal of Innovative Research in Computer Science & Technology, 10(2), 216–219. https://doi.org/10.55524/
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