The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.

中文翻译：

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使用卷积神经网络的基于内容的胃图像检索

内窥镜检查程序已证明在检测胃部病变方面非常有效，每天全球产生大量的内窥镜检查图像。基于内容的胃图像检索（CBGIR）系统已证明在胃图像分析中具有巨大潜力。胃癌患者中的胃癌前病变（GPD）患病率较高。因此，有效的干预措施在GPD阶段至关重要。在本文中，提出了一种使用改进的ResNet-18的CBGIR方法来生成二进制哈希码，以实现快速，准确的图像检索过程。我们测试了几种流行的模型（AlexNet，VGGNet和ResNet），并将ResNet-18确定为最佳选择。我们建议的方法使用GPD数据集进行了评估，得出的分类精度为96.21±0.66％平均平均精度为0.927±0.006，优于其他最新的传统方法。此外，我们基于特征表示构造了胃图（GM），以可视化检索结果。这项工作对于内镜医师在理解典型的GPD特征和改善辅助诊断方面具有重要的辅助意义。