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Programmed Multi-Classification of Brain Tumor Images Using Deep Neural Network

机译：使用深度神经网络对脑肿瘤图像进行编程的多分类

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Identification of brain tumors attends a critical role in evaluating tumors and making decisions about care as per their grades. Several imaging methods are employed to identify brain tumors. Though, leading to its excellent image quality and the reality that it depends on no cosmic radiation, MRI is widely utilized. Deep learning (DL) is a computer vision field of study and has shown remarkable output currently, notably in classification and segmentation issues. This article proposes, DL design based on a Convolution Neural Network (CNN) to identify various types of brain tumors leveraging two publicly accessible resources or databases. The previous identify tumors into (Meningioma, Glioma, and Pituitary tumors). Another one distinguishes between all three categories (Grade II, Grade III, and Grade IV).
机译：脑肿瘤的鉴定在评估肿瘤和根据其等级做出护理决策方面起着至关重要的作用。几种成像方法被用来识别脑肿瘤。但是，由于其出色的图像质量和不依赖宇宙辐射的现实，MRI被广泛使用。深度学习（DL）是计算机视觉的研究领域，目前显示出令人瞩目的成果，尤其是在分类和细分问题上。本文提出了基于卷积神经网络（CNN）的DL设计，以利用两个可公共访问的资源或数据库来识别各种类型的脑肿瘤。以前将肿瘤分为（脑膜瘤，神经胶质瘤和垂体瘤）。另一个区分所有三个类别（II级，III级和IV级）。

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