使用深度学习神经网络对脑肿瘤进行分类

Heba Mohsen , El-Sayed A. El-Dahshan , El-Sayed M. El-Horbaty , Abdel-Badeeh M. Salem
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引用次数: 627

摘要

深度学习是一个新的机器学习领域,在过去的几年里获得了很多兴趣。它被广泛应用于几个应用程序,并被证明是许多复杂问题的强大机器学习工具。在本文中,我们使用深度神经网络分类器(DL架构之一)将66个脑mri数据集分为4类,即正常、胶质母细胞瘤、肉瘤和转移性支气管癌肿瘤。该分类器将离散小波变换(DWT)、强大的特征提取工具和主成分分析(PCA)相结合,在所有性能指标上的性能评价都很好。
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Classification using deep learning neural networks for brain tumors

Deep Learning is a new machine learning field that gained a lot of interest over the past few years. It was widely applied to several applications and proven to be a powerful machine learning tool for many of the complex problems. In this paper we used Deep Neural Network classifier which is one of the DL architectures for classifying a dataset of 66 brain MRIs into 4 classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the evaluation of the performance was quite good over all the performance measures.

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