Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

C S Sonali, John Kiran, B S Chinmayi, K V Suma, Muhammad Easa
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Abstract

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.

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基于变压器的肺部听诊声音精确分类网络。
呼吸系统疾病是世界范围内死亡的主要原因,影响着相当大比例的肺功能异常人群,而肺功能异常可能导致呼吸系统疾病。早期发现和预防对于有效管理这些疾病至关重要。深度学习算法为分析复杂的医学数据和帮助早期疾病检测提供了一种很有前途的方法。虽然基于变换器的序列分类模型已被证明对情感分析、主题分类等任务有效,但它们在呼吸道疾病分类方面的潜力在很大程度上仍未被探索。本文提出了一种利用转换器-编码器块的分类器,该分类器可以捕获医学数据中的复杂模式和依赖关系。所提出的模型在2017年国际生物医学健康信息学会议的大型数据集上进行了训练和评估,取得了最先进的结果,平均灵敏度为70.53%,平均特异性为84.10%,平均得分为77.32%,平均谐波得分为76.10%。这些结果证明了该模型在诊断呼吸道疾病方面的有效性,同时占用了最少的计算资源。
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