Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model

TL;DR

This paper introduces an attention-guided deep learning model for brain tumor classification in MR images, effectively distinguishing tumor presence and source with high accuracy, aiding clinical diagnosis.

Contribution

The paper proposes a novel attention-guided CNN model that improves tumor classification accuracy and can identify tumor source types, addressing limitations of prior primary tumor-focused research.

Findings

Achieved 99.18% accuracy in tumor presence detection

Achieved 83.38% accuracy in tumor source classification

Model performance aligns with medical expert assessments

Abstract

In the clinical diagnosis and treatment of brain tumors, manual image reading consumes a lot of energy and time. In recent years, the automatic tumor classification technology based on deep learning has entered people's field of vision. Brain tumors can be divided into primary and secondary intracranial tumors according to their source. However, to our best knowledge, most existing research on brain tumors are limited to primary intracranial tumor images and cannot classify the source of the tumor. In order to solve the task of tumor source type classification, we analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Meanwhile, the method proposed in this paper also effectively improves the accuracy of classifying the presence or absence of tumor. For the brain MR dataset, our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor, and 83.38% for classifying the source of tumor. Experimental results show that our method is consistent with the method of medical experts. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.