用CNN分析MRI图像，98%准确率识别脑肿瘤
A CNN Approach to Automated Detection and Classification of Brain Tumors
- 用各向异性扩散滤波去噪，再用CNN分类
- 在3264张MRI图上，EfficientNet达到98%准确率
- 适合医学影像分析与自动化诊断研究者
脑肿瘤需及时诊断以确保有效治疗。形态特征如大小、位置、纹理和表现差异使肿瘤检测复杂化，医学影像常存在噪声和不完整问题。本文提出一种处理磁共振成像（MRI）数据的方法，涵盖图像分类与去噪技术。利用MRI可帮助医生发现脑部疾病。研究旨在通过分析提供的MRI数据，区分健康脑组织与脑肿瘤。相比计算机断层扫描（CT），MRI能更清晰呈现内部解剖结构，更适合脑肿瘤数据分析。首先对MRI图像使用各向异性扩散滤波进行去噪，模型训练采用公开且经过验证的脑肿瘤分类（MRI）数据库，包含3,264张脑部MRI图像。通过SMOTE实现数据增强与数据集平衡。采用ResNet152V2、VGG、ViT和EfficientNet等卷积神经网络进行分类，其中EfficientNet达到最高98%的准确率。
原文摘要 · Abstract (English)
Brain tumors require an assessment to ensure timely diagnosis and effective patient treatment. Morphological factors such as size, location, texture, and variable appearance complicate tumor inspection. Medical imaging presents challenges, including noise and incomplete images. This research article presents a methodology for processing Magnetic Resonance Imaging (MRI) data, encompassing techniques for image classification and denoising. The effective use of MRI images allows medical professionals to detect brain disorders, including tumors. This research aims to categorize healthy brain tissue and brain tumors by analyzing the provided MRI data. Unlike alternative methods like Computed Tomography (CT), MRI technology offers a more detailed representation of internal anatomical components, making it a suitable option for studying data related to brain tumors. The MRI picture is first subjected to a denoising technique utilizing an Anisotropic diffusion filter. The dataset utilized for the models creation is a publicly accessible and validated Brain Tumour Classification (MRI) database, comprising 3,264 brain MRI scans. SMOTE was employed for data augmentation and dataset balancing. Convolutional Neural Networks(CNN) such as ResNet152V2, VGG, ViT, and EfficientNet were employed for the classification procedure. EfficientNet attained an accuracy of 98%, the highest recorded.
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