Multi-Classification of brain tumor based on deep CNN

Wadhah Ayadi, W. Elhamzi, M. Atri
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Abstract

In the last decades, brain tumors are considered one of the mortal cancers in the world. The right tumors detection and identification in the early phases have a significant role to select an accurate treatment. Due to the increasing number of patients and brain tumor types, the manual analyses of Magnetic Resonance Imaging (MRI) images represent a tiring routine and can lead to human errors. In the goal to surpass these problems, an automatic CAD system is needed. We discussed, in this paper, a new model to classify brain tumors using CNN. The suggested scheme is experimentally evaluated on a public dataset. The proposed approach yields a convincing performance compared to previous techniques based on the experimental results.
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基于深度CNN的脑肿瘤多分类
在过去的几十年里,脑瘤被认为是世界上最致命的癌症之一。早期正确的肿瘤检测和识别对于选择正确的治疗方案具有重要意义。由于患者数量和脑肿瘤类型的增加,人工分析磁共振成像(MRI)图像是一项累人的工作,可能导致人为错误。为了超越这些问题,需要一个自动化的CAD系统。本文讨论了一种利用CNN对脑肿瘤进行分类的新模型。该方案在一个公共数据集上进行了实验评估。实验结果表明,与以往的方法相比,该方法具有令人信服的性能。
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