轻量CNN模型实现98.78%脑瘤分类准确率，助力早期诊断。
Light Weight CNN for classification of Brain Tumors from MRI Images
- 设计轻量CNN，结合图像预处理与超参数调优
- 在四类脑瘤数据上达98.78%分类准确率
- 适合临床辅助诊断，模型复杂度低易部署
本研究提出一种基于卷积神经网络（CNN）的方法，用于磁共振成像（MRI）扫描中脑瘤的多类别分类。使用公开数据集，包含四类：胶质瘤、脑膜瘤、垂体瘤和无肿瘤。目标是构建一个轻量级深度学习模型，实现高精度自动分类。通过图像归一化、数据增强及裁剪技术减少背景噪声，突出关键区域。利用Keras Tuner进行超参数调优，系统探索网络结构。采用5折交叉验证评估模型，每个配置在多个数据划分上测试以降低过拟合风险。实验结果表明，该模型达到98.78%的分类准确率，具备临床辅助诊断潜力。所提方法为早期脑瘤诊断提供低复杂度且高效的解决方案。
原文摘要 · Abstract (English)
This study presents a convolutional neural network (CNN)-based approach for the multi-class classification of brain tumors using magnetic resonance imaging (MRI) scans. We utilize a publicly available dataset containing MRI images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. Our primary objective is to build a light weight deep learning model that can automatically classify brain tumor types with high accuracy. To achieve this goal, we incorporate image preprocessing steps, including normalization, data augmentation, and a cropping technique designed to reduce background noise and emphasize relevant regions. The CNN architecture is optimized through hyperparameter tuning using Keras Tuner, enabling systematic exploration of network parameters. To ensure reliable evaluation, we apply 5-fold cross-validation, where each hyperparameter configuration is evaluated across multiple data splits to mitigate overfitting. Experimental results demonstrate that the proposed model achieves a classification accuracy of 98.78%, indicating its potential as a diagnostic aid in clinical settings. The proposed method offers a low-complexity yet effective solution for assisting in early brain tumor diagnosis.
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