车间机器学习克拉西卡肿瘤奥塔克帕特拉核磁共振蒙古纳坎卷积神经网络丹支持向量机

A. Minarno, Denar Regata Akbi, Yuda Munarko
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引用次数: 0

摘要

大脑是人类的重要器官之一。脑瘤可能对人类构成威胁。因此,研究人员开发了一种CNN方法,经测试对检测脑肿瘤有效。CNN是一种非常流行的方法,在其应用中,它被用于图像分类和其他几个图像处理案例。CNN可以在人工神经网络上更好地检测和识别图像中的物体。此外,许多研究者还使用SVM方法,将SVM应用于图像处理的情况下进行模式识别。脑瘤可能是由于癌症在另一个身体部位扩散而引起的。根据世界卫生组织(WHO)的一份报告,脑癌占其他癌症的比例不到2%,但其严重的发病率和由此产生的并发症是巨大的。脑癌需要多学科的治疗,因此需要一个专业的标准政策来优化治疗。本次活动提出了一个使用CNN和SVM在MRI图像中进行脑肿瘤分类的机器学习研讨会,在CNN活动中可以分为CNN建模、数据预处理、构建和实现CNN模型等几个部分,在SVM中教授如何构建一个超平面。这一活动由穆罕默德玛琅大学校友在各自领域的专家发言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WORKSHOP MACHINE LEARNING KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE
The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.
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