Deep learning technologies show an impressive performance in the machine learning community for biomedical image detection, segmentation, and classification. They become the widely used computational approach to achieve complex tasks matching to humans. Understanding the applications, algorithms, and challenges is crucial for the development of DL-based architectures. Massive data learning is one of the main advantages of deep learning. In this paper, we provide a comprehensive review of deep CNN architectures—LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet—highlighting elements of the particular model along with architectures. Improvements of the one model over the previous model were also discussed. Though they are classic and old-fashioned architectures, when they integrate with one or more other CNN models, they yield significant results in the field of disease detection and classification. This paper outlines the current work that carried out by these models in biomedical field recently and finally concludes with a comparative summary of all five models that are discussed.