Automated Grading of Glioma Using Deep Neural Networks

Year 2023, , 15 - 22, 30.12.2023

Muhammed Yıldırım , Serpil Aslan , Emine Cengil , Sercan Yalçın

https://doi.org/10.46572/naturengs.1397010

Abstract

Gliomas are one of the most common tumors in the brain. It is possible to grade gliomas as Lower-Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). Clinical and molecular/mutation factors come to the fore in the grading of gliomas. Molecular tests used to grade glioma are expensive and time consuming. In this study, deep learning networks were used for glioma grading. Long short-term memory (LSTM) and Convolutional neural network (CNN) were used together in the proposed model. The developed model was also compared with 6 different classifiers accepted in the literature. Among the models used in the study, the developed model achieved the highest performance. In this study, glioma grading was performed for the purpose of improving performance and reducing costs.

Keywords

Classifiers, CNN, Deep Learning, Glioma, LSTM

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