轻量CNN精准识别脑瘤，准确率超99%且计算开销小。
Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network
- 设计轻量级CNN，通过高效特征提取提升分类性能。
- 在两个数据集上准确率达99.03%~99.28%，ROC超99.88%。
- 适合临床部署，比主流模型更省资源，适合医疗场景。
提高患者预后依赖于脑瘤的快速准确诊断，但人工分析MRI耗时且不可靠。尽管深度学习有潜力，现有模型多计算密集，难以应对脑瘤类型复杂多样。本文提出一种轻量高效卷积神经网络（CNN），用于多类脑瘤分类，涵盖胶质瘤、脑膜瘤、垂体瘤及健康样本。模型在Figshare和Kaggle两个公开数据集上评估，分别取得99.03%和99.28%的分类准确率，以及99.88%和99.94%的ROC值。相比DenseNet201、MobileNetV2、VGG19、Xception、InceptionV3、ResNet50等先进模型，本方法参数更少，计算开销更低，性能更优。结果表明该模型具临床实用价值，可作为可靠的辅助诊断工具。
原文摘要 · Abstract (English)
Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumor) instances. The model was rigorously evaluated on two publicly accessible datasets from Figshare and Kaggle. Leveraging efficient feature extraction and optimized training strategies, our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures. In contrast to cutting-edge models like DenseNet201, MobileNetV2, VGG19, Xception, InceptionV3, and ResNet50, our approach consistently demonstrated superior performance with reduced computational overhead. These findings highlight the potential of the proposed model as a practical and reliable diagnostic aid in clinical environments.
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