A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images

Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan