Introduction

Bronchoscopy is of great significance in diagnosing and treating respiratory illness. Using deep learning, a diagnostic system for bronchoscopy images can improve the accuracy of tracheal, bronchial, and pulmonary disease diagnoses for physicians and ensure timely pathological or etiological examinations for patients. Improving the diagnostic accuracy of the algorithms remains the key to this technology.

Objectives

To deal with the problem, we proposed a multiscale attention residual network (MARN) for diagnosing lung conditions through bronchoscopic images. The multiscale convolutional block attention module (MCBAM) was designed to enable accurate focus on lesion regions by enhancing spatial and channel features. Gradient-weighted Class Activation Map (Grad-CAM) was provided to increase the interpretability of diagnostic results.

Methods

We collected 615 cases from Harbin Medical University Cancer Hospital, including 2900 images. The dataset was partitioned randomly into training sets, validation sets and test sets to update model parameters, evaluate the model’s training performance, select network architecture and parameters, and estimate the final model. In addition, we compared MARN with other algorithms. Furthermore, three physicians with different qualifications were invited to diagnose the same test images, and the results were compared to those of the model.

Results

In the dataset of normal and lesion images, our model displayed an accuracy of 97.76% and an AUC of 99.79%. The model recorded 92.26% accuracy and 96.82% AUC for datasets of benign and malignant lesion images, while it achieved 93.10% accuracy and 99.02% AUC for normal, benign, and malignant lesion images.

Conclusion

These results demonstrated that our network outperforms other methods in diagnostic performance. The accuracy of our model is roughly the same as that of experienced physicians and the efficiency is much higher than doctors. MARN has great potential for assisting physicians with assessing the bronchoscopic images precisely.

中文翻译：

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使用深度学习对支气管镜诊断图像进行呼吸系统疾病的准确预测

 介绍

支气管镜检查在诊断和治疗呼吸系统疾病方面具有重要意义。使用深度学习，支气管镜图像诊断系统可以提高医生对气管、支气管和肺部疾病诊断的准确性，并确保为患者及时进行病理或病因检查。提高算法的诊断准确性仍然是这项技术的关键。

 目标

为了解决这个问题，我们提出了一个多尺度注意力残差网络 （MARN），用于通过支气管镜图像诊断肺部疾病。多尺度卷积块注意力模块 （MCBAM） 旨在通过增强空间和通道特征来实现对病变区域的准确关注。提供梯度加权类激活图 （Grad-CAM） 以提高诊断结果的可解释性。

 方法

我们从哈尔滨医科大学附属肿瘤医院收集了 615 例病例，包括 2900 张图像。数据集被随机划分为训练集、验证集和测试集，以更新模型参数、评估模型的训练性能、选择网络架构和参数并估计最终模型。此外，我们将 MARN 与其他算法进行了比较。此外，邀请了 3 名具有不同资格的医生诊断相同的测试图像，并将结果与模型的结果进行比较。

 结果

在正常和病变图像的数据集中，我们的模型显示准确率为 97.76%，AUC 为 99.79%。该模型对良恶性病灶图像数据集的准确率为 92.26%，AUC 为 96.82%，而正常、良性和恶性病灶图像的准确率为 93.10%，AUC 为 99.02%。

 结论

这些结果表明，我们的网络在诊断性能方面优于其他方法。我们模型的准确性与经验丰富的医生大致相同，效率也远高于医生。MARN 在帮助医生精确评估支气管镜图像方面具有巨大潜力。