ON METHODS OF THE BRAIN TUMOR RECOGNITION WITH DEEP LEARNING METHODS

UDC 519.6
DOI: 10.36871/2618-9976.2022.08.008

Authors

Eugeny Yu. Shchetinin
Doctor of Physics and Mathematics, Professor, Professor of the Department of Mathematics, Moscow, Russia

Abstract

In this paper computer studies of the effectiveness of using transfer learning methods to solve the problem of recognizing human brain tumor based on MRI images were performed. The deep convolutional networks VGG16, ResNet50, InceptionV3, Xception, DenseNet121 and MobileNet_v2 were used as the basic pretrained models. Various training and finetuning strategies for deep convolutional networks for recognizing brain tumor are proposed. An analysis of their performance showed that the strategy of finetuning of the Xception model on an extended MRIscans data set yielded higher accuracy, precision, recall and AUC metrics compared to the other models. The best classification achieved accuracy on the Xception finetuned model is 98%.

Keywords

Brain tumor, MRIscans, Deep convolutional networks, Transfer learning