Brain Tumor Detection System using Convolutional Neural Network

Shubham Koshti, Varsha N. Degaonkar, Ishan Modi, Ishan Srivastava, Janhavi Panambor, Anjali Jagtap
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引用次数: 1

Abstract

Brain tumors, in medical terms, are the intentional or unintentional growth of mass cells which hamper the conventional functioning of the shape of a brain. For correct diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in the early stages. The tumor within the brain is one of the most dangerous diseases and might be diagnosed easily and reliably with the assistance of detection of the tumor using automated techniques on MRI Images. Positron Emission Tomography, Cerebral Arteriogram, spinal tap, and Molecular testing are used for tumor detection. Digital image processing plays an important role in the analysis of medical images. Segmentation of tumors involves the separation of abnormal brain tissues from normal tissues of the brain. Over the few past years, various researchers have proposed semi and fully-automatic methods for the detection and segmentation of Brain tumors. The motivation behind the paper is to detect neoplasm and supply the better treatment for the suffering. The objectives of the paper are to develop an end-product (Web Application) that can be installed at hospitals. To facilitate this a detection model is developed that may accurately predict if an uploaded MRI scan of the brain shows it is affected by a tumor or not. To implement the paper a Convolutional Neural Network(CNN) was used to define the model. Transfer Learning is implemented to efficiently train the model. The data set used is split into 3 sets which are train, test and validation, in the ratio 80:10:10. The model is meant to be trained for 12 epochs. Callbacks also have been given to automate the model save process. The test accuracy of 97% is achieved. This trained model will be connected with an online Application via API. Within the proposed Web App the user is having access to four routes; which is a welcome page and which contains information about the system, the second route is information and awareness about the brain tumor in medical terms, third is the detection page, in which the trained model is deployed. The user can provide an input image, MRI images in our case, and the last route is the team information. Images which are fed to the model route will be processed by the developed convolutional neural network which can then confirm if a tumor is present or not and intimidate the user for the same through an output Display. The advantage of using this system is that it will automate the detection process, and ease the workload of the hospital staff. However for the advantage to become a reality, careful selection of accurate data is needed, or else there is a chance of false results.
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基于卷积神经网络的脑肿瘤检测系统
在医学术语中,脑瘤是指大量细胞有意或无意地生长,妨碍了大脑形状的常规功能。为了正确诊断和制定有效的治疗计划,早期发现脑肿瘤是必要的。脑内肿瘤是最危险的疾病之一,在MRI图像的自动检测技术的帮助下,可以轻松可靠地诊断肿瘤。正电子发射断层扫描、脑动脉造影、脊髓穿刺和分子检测用于肿瘤检测。数字图像处理在医学图像分析中起着重要的作用。肿瘤分割包括将异常脑组织与正常脑组织分离。在过去的几年中,各种研究人员提出了半自动和全自动的脑肿瘤检测和分割方法。这篇论文背后的动机是检测肿瘤并为患者提供更好的治疗。本文的目标是开发一个可以在医院安装的最终产品(Web应用程序)。为了促进这一点,开发了一种检测模型,可以准确预测上传的大脑MRI扫描是否显示它受到肿瘤的影响。为了实现本文,使用卷积神经网络(CNN)来定义模型。采用迁移学习方法对模型进行有效训练。使用的数据集按80:10:10的比例分为训练、测试和验证三组。该模型将被训练12个时代。还提供了回调函数来自动化模型保存过程。测试准确率达到97%。此训练模型将通过API与在线应用程序连接。在建议的Web应用程序中,用户可以访问四条路由;这是一个欢迎页面,包含了关于系统的信息,第二条路径是医学术语中关于脑肿瘤的信息和意识,第三条是检测页面,其中部署了训练过的模型。用户可以提供一个输入图像,在我们的例子中是MRI图像,最后一个路径是团队信息。输入到模型路径的图像将由开发的卷积神经网络进行处理,然后可以确认是否存在肿瘤,并通过输出显示器恐吓用户。使用该系统的优点是实现了检测过程的自动化,减轻了医院工作人员的工作量。然而,为了使优势成为现实,需要仔细选择准确的数据,否则就有可能出现错误的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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