基于卷积神经网络和自然语言处理的肺癌检测系统

Jiahao Chen, Qianli Ma, Weixin Wang
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引用次数: 2

摘要

肺癌一直被认为是对人类威胁最大的疾病之一,早期发现恶性肿瘤对治疗至关重要。目前,医院放射科通常需要处理多幅CT图像进行检测,这给医生带来了巨大的工作量。在这里,我们提出了一个新的系统来帮助肺癌的检测。具体来说,利用基于深度特征的卷积神经网络(CNN)对肺癌肿瘤进行分类,准确率达到88%。此外,系统还嵌入了一个基于自然语言处理(NLP)技术的聊天机器人,以提供即时的知识和信息。这些结果揭示了如何在相当程度上减少医生的工作量。
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A Lung Cancer Detection System Based on Convolutional Neural Networks and Natural Language Processing
Lung Cancer has long been regarded as one of the most threatened diseases to human beings, and detecting early malignant tumors is of vital importance for treatment. Contemporarily, Radiology departments in hospitals usually have to deal with multiple CT images to carry out the detection, which is a huge workload for doctors. Here, we propose a novel system to help with lung cancer detection. Specifically, deep feature based convolutional neural networks (CNN) is applied to classify lung cancer tumors, realizing an accuracy of 88%. Moreover, a chatbot based on natural language processing (NLP) technology is embedded into the system to provide immediate knowledge and information. These results shed light on how doctors’ workload might be reduced to a considerable extent.
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