An Efficient Analysis of Various Deep learning Model in Brain tumor Detection with MRI

This paper compares three of the popular deep learning models being VGG-16, VGG-19 and ResNet50 with respect to how they perform in the classification task. We investigate how much each model learns, the reduction of errors and inferences from data over epochs, through an in-depth analysis of training and testing phases. The authors concluded that all the models perform efficiently; however, from the study results of performance and accuracy, VGG-19 and ResNet50 have a high-performance scale as well as super accuracy in their deep learning model which encourages business for application towards complex pattern recognition or predictive modelling. These results provide important intuitions on what deep learning architectures can do, and pave the path for further works in this area, b