Convolutional Neural Networks in Medical Imaging: A Review

This review article traces the trajectory of Convolutional Neural Networks, providing an in-depth analysis of their foundational components: convolutional, pooling, and fully connected layers, and their integral function in feature extraction and image classification. It offers a comprehensive survey of landmark architectures such as LeNet, AlexNet, GoogLeNet, VGGNet, ResNet, ShuffleNet, and EfficientNet, examining their variants and their significant contributions to medical image processing. The review corroborates the transformative impact of CNNs in pivotal medical applications, including cancer detection, Alzheimer's diagnosis, and brain tumor identification. Additionally, It also examines the current challenges and issues, and outlines potential future directions for the field's evolution.