An Analysis of Convolutional Neural Networks (International Journal of Innovative Research in Computer Science & Technology, 2022)
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.
Keywords: Convolutional Neural Networks; Deep Learning; Networks; Radiology; Supervised