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

**Authors:** Yuhao Zhang, Shuhang Wang, Haoxiang Wu, Kejia Hu, Shufan Ji  
**arXiv:** [2104.02331](https://arxiv.org/abs/2104.02331)  
**Date:** 2021-04-06  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2104.02331v1/

## Key points

- Manual image reading in the clinical diagnosis and treatment of brain tumors is time-consuming and energy-intensive.
- Automatic tumor classification technology based on deep learning has led to more efficient and accurate methods in recent years.
- Brain tumors are categorized into primary and secondary intracranial tumors based on their source.
- A gap exists in accurately classifying the source of primary intracranial tumors despite advancements in research.
- Researchers, including Yuhao Zhang, Shuhang Wang, Haoxiang Wu, Kejia Hu, and Shufan Ji, proposed an attention-guided deep CNN model for brain tumor classification in MR images.
- The proposed method achieved remarkable results with an average accuracy rate of 99.18% for detecting the presence or absence of a tumor and 83.38% for classifying the source of the tumor under ten-fold cross-validation.
- The developed model aligns closely with expert medical diagnosis methods and can potentially assist healthcare professionals in streamlining clinical decision-making processes related to brain tumors.
- This study represents a significant advancement in leveraging deep learning technology for improving efficiency and accuracy in diagnosing brain tumors.
- The attention-guided CNN model offers promising prospects for enhancing diagnostic capabilities in clinical settings and revolutionizing current practices in neuroimaging analysis.

## Summary

In the field of clinical diagnosis and treatment of brain tumors, manual image reading has been a time-consuming and energy-intensive process. However, with the emergence of automatic tumor classification technology based on deep learning in recent years, there has been a significant shift towards more efficient and accurate methods. Brain tumors are typically categorized into primary and secondary intracranial tumors based on their source. Despite advancements in research focusing on primary intracranial tumor images, there remains a gap in the ability to classify the source of the tumor accurately. To address this challenge, a team of researchers including Yuhao Zhang, Shuhang Wang, Haoxiang Wu, Kejia Hu, and Shufan Ji have proposed an attention-guided deep convolutional neural network (CNN) model for brain tumor classification in MR images. This innovative approach not only enhances the accuracy of identifying the presence or absence of a tumor but also aims to classify the source type of the tumor effectively. Through extensive experimentation using brain MR datasets, the proposed method achieved remarkable results. Under ten-fold cross-validation, it demonstrated an average accuracy rate of 99.18% for detecting the presence or absence of a tumor and 83.38% for classifying the source of the tumor. These findings indicate that the developed model aligns closely with expert medical diagnosis methods and can potentially assist healthcare professionals in streamlining their clinical decision-making processes related to brain tumors. Overall, this study represents a significant advancement in leveraging deep learning technology for improving the efficiency and accuracy of diagnosing brain tumors. The attention-guided CNN model offers promising prospects for enhancing diagnostic capabilities in clinical settings and holds great potential for revolutionizing current practices in neuroimaging analysis.