The detection of brain tumors in brain magnetic resonance imaging (MRI) image is an important process for preventing earlier death. This article proposes an automated computer aided method for detecting and locating the brain tumors in brain MRI images using deep learning algorithms. The proposed method has three sub modules as preprocessing, classifications and segmentation. In this article, data augmentation is used as preprocessing method. The preprocessed brain MRI images are classified into either tumor case or nontumor case using classification approach. In this brain tumor detection and segmentation process, convolutional neural networks (CNNs) classification architecture is used for classifying the brain images. The morphological based segmentation methodology is used in this article for segmenting the tumor regions in classified brain images. Further, the segmented tumor regions are diagnosed into “Mild” and “Severe” case using CNN architecture. The proposed methodology is applied on the brain images from open access dataset. The performance of the proposed system is analyzed in terms of sensitivity, specificity, and precision, F-score, Disc similarity index and tumor region segmentation accuracy on set of brain images. The simulation results of this proposed framework are verified by expert radiologist.

中文翻译：

---

使用深度学习卷积神经网络检测和诊断脑肿瘤

在脑磁共振成像（MRI）图像中检测脑肿瘤是预防早期死亡的重要过程。本文提出了一种自动计算机辅助方法，用于使用深度学习算法检测和定位脑 MRI 图像中的脑肿瘤。该方法具有预处理、分类和分割三个子模块。在本文中，数据增强被用作预处理方法。使用分类方法将预处理的脑MRI图像分类为肿瘤病例或非肿瘤病例。在这个脑肿瘤检测和分割过程中，卷积神经网络 (CNN) 分类架构用于对脑图像进行分类。本文使用基于形态学的分割方法对分类的脑图像中的肿瘤区域进行分割。此外，使用 CNN 架构将分割的肿瘤区域诊断为“轻度”和“重度”病例。所提出的方法应用于来自开放访问数据集的大脑图像。从灵敏度、特异性和精度、F 值、椎间盘相似性指数和脑图像集的肿瘤区域分割精度方面分析了所提出系统的性能。该框架的仿真结果得到了放射科专家的验证。从灵敏度、特异性和精度、F 值、椎间盘相似性指数和脑图像集的肿瘤区域分割精度方面分析了所提出系统的性能。该框架的仿真结果得到了放射科专家的验证。从灵敏度、特异性和精度、F 值、椎间盘相似性指数和脑图像集的肿瘤区域分割精度方面分析了所提出系统的性能。该框架的仿真结果得到了放射科专家的验证。