Deep Learning: Convolutional Neural Networks for Medical Image Analysis - A Quick Review

H. Sharif, Faisal Rehman, Amina Rida
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引用次数: 7

Abstract

Deep learning has quickly evolved over the past several years from a promising to a feasible solution for medical image analysis. This is a great topic for research because more and more people are using medical imaging to treat and diagnose. A significant benefit of deep learning is the ability to use enormous volumes of data to eliminate the painstaking hand-crafting of features, which needs strong domain expertise. This study discusses how convolutional neural networks (CNNs) are utilized in the field, including detection, classification, registration, segmentation, and picture enhancement. It also gives some important information about how CNNs can be used to analyze medical images of the brain, eye, breast, chest, and skin.
深度学习:用于医学图像分析的卷积神经网络-快速回顾
在过去的几年里,深度学习已经从一个有希望的医学图像分析解决方案迅速发展成为一个可行的解决方案。这是一个很好的研究课题,因为越来越多的人正在使用医学成像来治疗和诊断。深度学习的一个显著好处是能够使用大量数据来消除手工制作特征的辛苦工作,这需要强大的领域专业知识。本研究讨论了卷积神经网络(cnn)在检测、分类、配准、分割和图像增强等领域的应用。它还提供了一些关于cnn如何用于分析大脑、眼睛、乳房、胸部和皮肤的医学图像的重要信息。
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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