Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network.
Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network.
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DOI：
10.1038/s41598-021-83184-2
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发表时间：
2021-02-17
影响因子：
4.6
通讯作者：
Cui D
中科院分区：
文献类型：
--
作者：
Aslam MA;Xue C;Chen Y;Zhang A;Liu M;Wang K;Cui D
深度学习是一种新兴的工具，经常用于医学领域的疾病诊断。为早期胃癌的检测开辟了新的研究方向。计算机辅助诊断（CAD）系统由于其有效性而降低了死亡率。在这项研究中，我们提出了一种新的方法，使用堆叠稀疏自编码器的特征提取的判别特征的呼吸样本的未标记的数据。然后将Softmax分类器集成到所提出的特征提取方法中，从呼吸样本中对胃癌进行分类。准确地说，我们在每个光谱中识别出50个峰，以区分EGC、AGC和健康人。该CAD系统通过学习特征来减小输入和输出之间的距离，并且保留呼吸样本的输入数据集的结构。该方法从呼吸样本的未标记数据中提取特征。在完成无监督训练后，将具有Softmax分类器的自编码器级联以开发深度堆叠稀疏自编码器神经网络。最后，利用标记的训练数据对神经网络进行了微调，使模型更可靠和可重复。所提出的深度堆叠稀疏自编码器神经网络架构表现出优异的结果，对于晚期胃癌分类的总体准确率为98.7%，对于使用呼吸分析的早期胃癌检测的总体准确率为97.3%。此外，所开发的模型产生了良好的结果，召回率，精度和f评分值，使其适合于临床应用。
Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.
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基于表面增强拉曼散射传感器的呼吸分析可区分早期和晚期胃癌患者与健康人。
影响因子：
17.1
作者：
Chen, Yunsheng;Zhang, Yixia;Cui, Daxiang
通讯作者：
Cui, Daxiang
影响因子：
4
作者：
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通讯作者：
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影响因子：
17.1
作者：
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通讯作者：
Koscielny, Serge
影响因子：
--
作者：
KULLBACK, S;LEIBLER, RA
通讯作者：
LEIBLER, RA
DOI：
10.1017/atsip.2013.9
发表时间：
2014-01-01
影响因子：
3.2
作者：
Deng, Li
通讯作者：
Deng, Li