基于MRI图像的深度学习脑肿瘤分类

Shaveta Arora, Meghna Sharma
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引用次数: 7

摘要

磁共振成像(MRI)是一种主要的脑肿瘤可视化扫描方法。使用深度学习方法处理后,从MRI获得的详细图像有助于神经科医生对脑肿瘤进行分类。本文基于提取的特征对脑MRI图像进行探索性分析,并对不同的基于CNN的脑肿瘤MRI图像分类迁移学习模型进行对比分析。它展示了深度学习技术从大脑的核磁共振图像中检测脑癌的效率。性能是根据训练准确度和测试准确度来衡量的。这里的二元分类是无肿瘤和肿瘤分类。我们的研究目标是通过医学图像处理、模式分析、计算机视觉等多种技术对脑诊断进行增强、分割和分类,准确检测脑肿瘤并对其进行分类。
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Deep Learning for Brain Tumor Classification from MRI Images
Magnetic Resonance Imaging popularly known as MRI is one of the primary scans to visualize the brain tumor. The detailed pictures obtained from MRI when processed using deep learning methods help the neurologist in classifying brain tumor. The paper shows the exploratory analysis of brain MRI images based on extracted features and also a comparative analysis of different CNN based transfer learning models for the classification of MRI images for brain tumor. It shows the efficiency of deep learning techniques for the detection of brain cancer from the MRI images of the brain. The performance is measured in terms of training accuracy and test accuracy. Here binary classification is done with no tumor and with tumor classes. The goal of our study is to accurately detect tumors in the brain and classify it through the means of several techniques involving medical image processing, pattern analysis, and computer vision for enhancement, segmentation and classification of brain diagnosis.
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