【会议论文】基于网格搜索和坐标上升优化的卷积神经网络架构的脑癌MRI分类

November 1, 2025

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> 于2025年发表在2025 2nd International Conference on Intelligent Computing and Robotics (ICICR)会议论文集中。

## Title
Brain Cancer MRI Classification using CNN Architectures with Grid Search Enhanced with Coordinate Ascent Optimization

## Authors
Runhai He
Faculty of Applied Mathematics and Informatics, Belarusian State University, Minsk, Belarus

Boyi Li
Faculty of Digital Transformation, ITMO University, St. Petersburg, Russia

Zhenxing Zhang
Faculty of Applied Mathematics and Informatics, Belarusian State University, Minsk, Belarus

Quanhua Zhou
Faculty of Applied Mathematics and Informatics, Belarusian State University, Minsk, Belarus

Rui Liu
Faculty of Applied Mathematics and Informatics, Belarusian State University, Minsk, Belarus

Qingqing Song
Faculty of Applied Mathematics and Informatics, Belarusian State University, Minsk, Belarus
South China Institute of Frontier Science, Guangdong, China

## Abstract
Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively improve the accuracy and efficiency of tumor classification and provide an important reference for clinicians. Previous studies have shown that CNN has advantages in medical image classification. However, indepth discussion of the performance differences and hyperparameter optimization of different CNN models in brain cancer MRI image classification remains insufficient, limiting model selection and accuracy improvements in clinical applications. This study aims to provide a CNN model selection and optimization methodology for brain cancer MRI image classification to improve classification accuracy and reliability. The study selected CNN models of different levels and complexities, including LeNet-5, AlexNet and ResNet-18, and used the Grid Search Enhanced with Coordinate Ascent (GSECA) method to optimize hyperparameters, providing a feasible model selection and optimization methodology for brain cancer MRI image classification. Experimental results show that the method described in this paper achieved excellent results when using the Bangladesh Brain Cancer MRI Image Four-Classification Dataset (PMRAM) for classification. Based on the classification of the ResNet-18 model, the test accuracy reached 95.69%, and the F1-scores of the four categories of Glioma, Meningioma, Normal and Pituitary reached 94.33%, 94.07%, 96.12% and 98.21% respectively. The precision of Pituitary was as high as 99.10%. This result was significantly better than AlexNet and LeNet-5, and was at an excellent level in similar studies, achieving effective model screening. At the same time, it verified the importance of network depth and effective hyperparameter optimization to improve the performance of classification tasks in this scenario.

## Links
### IEEE Xplore
https://ieeexplore.ieee.org/abstract/document/11172983/

【会议论文】基于网格搜索和坐标上升优化的卷积神经网络架构的脑癌MRI分类

SOONG, CHINGTSING • 2025 年 11 月 01 日

【会议论文】基于网格搜索和坐标上升优化的卷积神经网络架构的脑癌MRI分类

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【会议论文】基于网格搜索和坐标上升优化的卷积神经网络架构的脑癌MRI分类

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【会议论文】基于网格搜索和坐标上升优化的卷积神经网络架构的脑癌MRI分类

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