快速轻量级肺结核检测:基于CNN的方法

Sangsaptak Pal, S. Mishra, B. P. Mishra, Santwana Sagnika, Saurabh Bilgaiyan
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引用次数: 0

摘要

在当前的场景中,卷积神经网络(CNN)得到了研究人员的关注。它是一种特殊类型的前馈神经网络,用于处理大图像。具有参数可调能力。然而,由于需要更多的训练时间,它的计算成本很高。因此,在本文中,我们有兴趣提出一种新的技术,它将减少CNN的训练参数数量,并提供一个有希望的精度。所提出的技术在肺结核的检测中得到了验证。
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Quick & Lightweight Tuberculosis Detection:A CNN Based Approach
In current scenario Convolutional Neural Network (CNN) has gained the attention of the researchers. It is a special type of feed forward neural network used to handle large images. It has the capability of adjusting the parameters. However, it is computationally expensive as it takes more training time. So in this paper we are interested to propose a new technique which will reduce the number of training parameters of CNN as well as providing a promising accuracy. The proposed technique is validated for the detection of tuberculosis.
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