Deep CNN Architectures for Medical and General Image Recognition

Comparative Analysis with AI-Enhanced Perspectives

Abstract

In image recognition, using Convolutional Neural Networks (CNNs), a computer can be trained to recognize images by learning in a hierarchical way from pixel information. In this paper, three CNN landmarks, VGGNet, ResNet and GoogLeNet, are studied and analyzed in terms of forward propagation, categorical cross-entropy loss functions, and gradient flow in back-propagation. In the field of medical imaging, an example of ResNet implementation is used for classification of brain tumors in MRI images. We also delve into the ways in which current AI developments[17-19], such as Vision Transformers, self-supervised learning, neural architecture search, and multimodal foundation models, are reshaping image recognition from traditional CNN methods.