Resnet101 and Googlenet Deep Learning Models: Comparing Success Levels in the Health Sector

Abstract

Artificial intelligence (AI) applications in the healthcare sector have revolutionized medical diagnosis and treatment. Advances in this field provide many advantages such as early detection of diseases and increasing the efficiency of healthcare services. In this study, in order to investigate the usability of deep learning models for tuberculosis (TB) detection, the accuracy rates of deep learning models such as ResNet101 and GoogLeNet are compared in terms of TB detection potential in the healthcare sector. The results of the analyses revealed that deep learning networks are successful in classifying chest X-ray images with and without TB. In addition, when the success levels were analyzed, it was determined that the ResNet101 deep learning network, with a success rate of 99.3%, showed a higher score than the other deep learning model considered in the study, GoogLeNet (98.2%). These findings obtained within the scope of the research reveal the importance and functionality of AI applications in order to increase diagnostic accuracy rates.