基于深度神经网络的肺癌医学图像识别

G. Jakimovski, D. Davcev
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引用次数: 21

摘要

医学图像(磁共振成像扫描)被医生和医学专家用来确定患者肺部是否存在癌症的可能性。我们正在使用这些图像,以及深度神经网络算法,通过训练深度神经网络(DNN)来识别肺癌,帮助医生进行图像诊断。我们的深度神经网络通过增加额外的卷积层和最大池化层来进行广泛的搜索,从而引入了新颖性。此外,我们正在使用进展缓慢的肺癌的图像来确定阈值或在进展的哪个点,我们的深度神经网络将诊断癌症。使用这种方法,医生将在早期发现和早期治疗肺癌方面获得额外的帮助。这些是我们研究的主要目的,包括彻底寻找肺癌的可能性和早期发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lung cancer medical image recognition using Deep Neural Networks
Medical images (Magnetic Resonance Imaging scans) are used by doctors and medical specialists to determine the possibility that a cancer is present in the lungs of a patient. We are using these images, along with Deep Neural Network algorithms to help doctors with image diagnostics by training the Deep Neural Network (DNN) to recognize lung cancer. Our Deep Neural Network introduces novelty by making extensive search by adding additional layers of convolution and max pooling. Moreover, we are using images from slow progressing lung cancer to determine the threshold or at which point in the progression, our Deep Neural Network, will diagnose the cancer. Using this, doctors will have additional help in early phase lung cancer detection and early treatment. These are the main purposes of our research, which includes thorough search of possibilities of lung cancer and early detection.
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