A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.
A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.
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DOI：
10.3390/mi14010053
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发表时间：
2022-12-25
期刊：
影响因子：
3.4
通讯作者：
Zhang, Zhidong
中科院分区：
文献类型：
--
作者：
Zhu, Xiaolong;Ma, Yuhang;Guo, Dong;Men, Jiuzhang;Xue, Chenyang;Cao, Xiyuan;Zhang, Zhidong
胃癌已经成为一个全球性的健康问题，严重影响人们的日常生活。胃癌患者的早期发现和及时治疗对保护人类健康具有重要意义。然而，常规的胃癌检查有并发症的风险，而且耗时。我们提出了一个框架，预测胃癌的非侵入性和方便。使用定制的舌图像捕获仪器采集了总共703个舌图像，然后创建了包含患有和不患有胃癌的受试者的数据集。由于该仪器采集的图像中含有非舌区域，因此采用Deeplabv3+网络进行舌分割，以减少特征提取中的干扰。提取了9个舌象特征，利用统计学方法和深度学习探索了舌象特征与胃癌的关系，设计了胃癌预测框架。实验结果表明，该框架具有较强的检测能力，准确率达到93.6%。通过结合统计方法和深度学习创建的胃癌预测框架提出了一种探索胃癌与舌特征之间关系的方案。该框架有助于胃癌患者的有效早期诊断。
Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.
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