基于Mobilenet的MRI图像的脑肿瘤类型分类

Tsamara Hanifa Arfan, Mardhiya Hayaty, Arifiyanto Hadinegoro
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引用次数: 6

摘要

核磁共振成像可以检测出含有脑瘤的软组织。磁共振成像在脑肿瘤中产生的成像,如果手工操作,不容易分析。结果需要更长的时间。深度学习是人工智能的一部分,可以自动分析数据。Mobilenet是深度学习中对医学图像进行分割处理的方法之一。移动网络是一种精度高、计算量少的CNN模型。因此,本研究提出使用移动网络架构对脑肿瘤类型进行分类。移动网络有多种分类。本研究发现的证据表明,移动网络的应用提高了整体准确性。移动网络类别中最好的结果是MobileNet V2 140×224,它达到了94%的准确率测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Brain Tumours Types Based On MRI Images Using Mobilenet
MRI can detect soft tissue that contains a brain tumour. Imaging produced by MRI in brain tumours can not be analyzed easily if done manually. Results in a longer time required. Deep learning is part of artificial intelligence that can analyze data automatically. Mobilenet is one of the methods in deep learning that functions to perform the segmentation process of medical images. Mobile Network is a CNN model with high accuracy and less computation. Therefore, this study proposes the use of Mobile Network architecture to classify brain tumour types. Mobile Network there are various categories. This study finds evidence that the application of Mobile networks improves overall accuracy. The best result from the Mobile Network category was MobileNet V2 140×224, which achieved an accuracy test of 94%.
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