医学图像处理中基于深度学习的脑肿瘤识别与分类研究

S. Karpakam, N. Senthilkumar, R. Kishorekumar, U. Ramani, P. Malini, S. Irfanbasha
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引用次数: 2

摘要

脑瘤是异常脑细胞的生长,其中一些可能发展成癌症。磁共振成像(MRI)扫描是检测脑肿瘤最常用的方法。在MRI图像上可以看到大脑的异常组织生长。在许多研究出版物中,深度学习和机器学习技术被用于识别脑肿瘤。当这些算法应用于核磁共振成像图像时,预测脑肿瘤只需要很短的时间,而且精确度的提高使患者治疗变得更简单。由于这些预测,放射科医生可以快速做出决定。建议的方法采用深度学习、卷积神经网络(CNN)、人工神经网络(ANN)、自定义神经网络和脑肿瘤的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor.
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