Du Wenju, Rao Nini, Dong Changlong, Wang Yingchun, Hu Dingcan, Zhu Linlin, Zeng Bing, Gan Tao
Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Biomed Opt Express. 2021 May 3;12(6):3066-3081. doi: 10.1364/BOE.420935. eCollection 2021 Jun 1.
The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.
准确诊断不同阶段的各种食管疾病对于制定精准治疗方案和提高食管癌患者的5年生存率至关重要。对胃镜图像中的各种食管疾病进行自动分类可以帮助医生提高诊断效率和准确性。现有的基于深度学习的分类方法只能同时对极少数类别的食管疾病进行分类。因此，我们提出了一种新型高效通道注意力深度密集卷积神经网络（ECA-DDCNN），它可以将食管胃镜图像分为四个主要类别，包括正常食管（NE）、癌前食管疾病（PEDs）、早期食管癌（EEC）和晚期食管癌（AEC），涵盖六种常见的食管疾病子类别和一种正常食管（共七个子类别）。我们从4077名患者中收集了20965张胃镜图像，用于训练和测试我们提出的方法。大量实验结果令人信服地表明，我们提出的ECA-DDCNN优于其他现有方法。我们方法的分类准确率（Acc）为90.63%，平均曲线下面积（AUC）为0.9877。与其他现有方法相比，我们的方法在各种食管疾病的分类中表现出更好的性能。特别是对于这些具有相似黏膜特征的食管疾病，我们的方法也实现了更高的真阳性（TP）率。总之，我们提出的分类方法已证实其在各种食管疾病诊断中的潜在能力。
