基于深度学习的脑MRI肿瘤检测

A. Anil, Aditya Raj, H. Aravind Sarma, N. R, Deepa P L
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引用次数: 12

摘要

卫生专家越来越多地利用大多数现代技术的好处,从而在卫生保健领域产生了可扩展的改进。正因为如此,出现了从手动监视到更精确的虚拟监视的范式转变,并且错误的百分比最小。人工智能(AI)的进步为医学成像技术带来了令人兴奋的高精度解决方案,是增强未来应用的关键方法。脑肿瘤的检测是医学领域中一项非常困难的任务。人工检测脑肿瘤耗时长,需要大量mri图像进行肿瘤诊断。因此,需要从脑磁共振图像中自动检测脑肿瘤。深度学习方法可以实现这一任务。不同的深度学习网络可以用于脑肿瘤的检测。该方法包括一个分类网络,该网络将输入的MR图像分为两类:第一类是有肿瘤的,第二类是没有肿瘤的。在这项工作中,脑肿瘤的检测是通过使用迁移学习技术重新训练分类器来进行分类的。实验结果表明,该方法优于现有方法。
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Brain Tumor detection from brain MRI using Deep Learning
Health experts are increasingly taking advantage of the benefits of most modern technologies, thus generating a scalable improvement in the area of health care. Because of this, there is a paradigm shift from manual monitoring towards more accurate virtual monitoring with minimum percentage of error. Advances in artificial intelligence (AI) led to exciting solutions with high accuracy for medical imaging technology and is a key method for enhancing future applications. Detection of brain tumor is a very difficult task in medical field. Detection of brain tumor manually is time consuming and requires large number of mri images for cancer diagnosis. So, there is a need for automatic brain tumor detection from Brain MR images. Deep learning methods can achieve this task. Different deep learning networks can be used for the detection of brain tumors. The proposed method comprises of a classification network which classifies the input MR images into 2 classes: on with tumor and the second without tumor. In this work, detection of brain tumor is done via classification by retraining the classifier using the technique known as transfer learning. The obtained result shows that our method outperforms the existing methods.
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