Crisdayanti Ida A P A, Nam Sung Woo, Jung Seong Kwan, Kim Seong-Eun
Department of Applied Artificial IntelligenceSeoul National University of Science and Technology Seoul 01811 South Korea.
Woorisoa Children's Hospital Seoul 08291 South Korea.
IEEE Open J Eng Med Biol. 2024 May 16;5:383-392. doi: 10.1109/OJEMB.2024.3402139. eCollection 2024.
In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has become increasingly crucial. Traditional diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), while accurate, often face accessibility challenges. Lung auscultation, a simpler alternative, is subjective and highly dependent on the clinician's expertise. The pandemic has further exacerbated these challenges by restricting face-to-face consultations. This study aims to overcome these limitations by developing an automated respiratory sound classification system using deep learning, facilitating remote and accurate diagnoses. We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. Our model is enhanced with attention feature fusion of low-to-high-level information based on a knowledge propagation mechanism to increase classification effectiveness. This novel approach was evaluated using the ICBHI benchmark dataset and a larger, self-collected Pediatric dataset comprising outpatient children aged 1 to 6 years. The proposed CNN model with knowledge propagation demonstrated superior performance compared to existing state-of-the-art models. Specifically, our model showed higher sensitivity in detecting abnormalities in the Pediatric dataset, indicating its potential for improving the accuracy of respiratory disease diagnosis. The integration of a knowledge propagation mechanism into a CNN model marks a significant advancement in the field of automated diagnosis of respiratory disease. This study paves the way for more accessible and precise healthcare solutions, which is especially crucial in pandemic scenarios.
鉴于新冠疫情，呼吸系统疾病的早期诊断变得愈发关键。传统诊断方法，如计算机断层扫描（CT）和磁共振成像（MRI），虽准确，但常常面临可及性挑战。肺部听诊作为一种更简单的替代方法，具有主观性，且高度依赖临床医生的专业知识。疫情通过限制面对面会诊进一步加剧了这些挑战。本研究旨在通过开发一种使用深度学习的自动呼吸音分类系统来克服这些限制，以促进远程且准确的诊断。我们开发了一种深度卷积神经网络（CNN）模型，该模型在图像分类框架内利用呼吸音的频谱表示。我们的模型基于知识传播机制，通过低到高层面信息的注意力特征融合进行增强，以提高分类效果。使用ICBHI基准数据集以及一个更大的、自行收集的包含1至6岁门诊儿童的儿科数据集对这种新方法进行了评估。与现有的最先进模型相比，所提出的具有知识传播功能的CNN模型表现出卓越的性能。具体而言，我们的模型在检测儿科数据集中的异常方面显示出更高的灵敏度，表明其在提高呼吸系统疾病诊断准确性方面的潜力。将知识传播机制集成到CNN模型中标志着呼吸系统疾病自动诊断领域的一项重大进展。本研究为更易获得且精确的医疗保健解决方案铺平了道路，这在疫情情况下尤为关键。
