Gliomas are the most common primary tumor of the brain and are classified into grades I‐IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non‐invasively distinguishing low‐grade (Grades II and III) glioma (LGG) and high‐grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs).

中文翻译：

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使用深度卷积神经网络对传统 MRI 图像进行自动神经胶质瘤分级。

神经胶质瘤是最常见的脑部原发性肿瘤，世界卫生组织 (WHO) 根据其侵袭性组织学外观将其分为 I-IV 级。胶质瘤分级对于确定治疗方案和预后预测起着重要作用。在这项研究中，我们提出了两种新方法，通过使用深度卷积神经网络，在传统 MRI 图像上自动、非侵入性地区分低级别（II 级和 III 级）神经胶质瘤（LGG）和高级别（IV 级）神经胶质瘤（HGG） （CNN）。