Brain Tumor Classification Using Medial Residual Encoder Layers

TL;DR

This paper introduces a deep learning-based system with medial residual encoder layers for accurate brain tumor classification in MRI images, achieving high accuracy with limited data.

Contribution

The study proposes a novel medial residual encoder architecture that improves brain tumor classification accuracy over existing methods.

Findings

Achieved 95.98% classification accuracy on MRI dataset.

Outperformed previous models on the same dataset.

Demonstrated effectiveness with limited training data.

Abstract

According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. Therefore, accurate and timely diagnosis of brain tumors will lead to more effective treatments. Physicians classify brain tumors only with biopsy operation by brain surgery, and after diagnosing the type of tumor, a treatment plan is considered for the patient. Automatic systems based on machine learning algorithms can allow physicians to diagnose brain tumors with noninvasive measures. To date, several image classification approaches have been proposed to aid diagnosis and treatment. For brain tumor classification in this work, we offer a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning. Our approach shows promising results by improving the tumor classification accuracy in Magnetic resonance imaging (MRI) images using a limited medical image dataset. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.