Akila Serag Mohamed, Imanov Elbrus, Almezhghwi Khaled
Department of Biomedical Engineering, Near East University, Mersin 10, 99138 Nicosia, Turkey.
Department of Computer Engineering, Near East University, Mersin 10, 99138 Nicosia, Turkey.
Diagnostics (Basel). 2023 Jun 28;13(13):2199. doi: 10.3390/diagnostics13132199.
The world's population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and evaluation. Although human experts are primarily tasked with the diagnosis of different medical conditions, artificial intelligence (AI)-assisted diagnoses have become considerably useful in recent times. One of the critical lung infections, which requires early diagnosis and subsequent treatment to reduce the mortality rate, is pneumonia. There are different methods for obtaining a pneumonia diagnosis; however, the adoption of chest X-rays is popular since it is non-invasive. The AI systems for a pneumonia diagnosis using chest X-rays are often built on supervised machine-learning (ML) models, which require labeled datasets for development. However, collecting labeled datasets is sometimes infeasible due to constraints such as human resources, cost, and time. As such, the problem that we address in this paper is the unsupervised classification of pneumonia using unsupervised ML models including the beta-variational convolutional autoencoder (β-VCAE) and other variants, such as convolutional autoencoders (CAE), denoising convolutional autoencoders (DCAE), and sparse convolutional autoencoders (SCAE). Namely, the pneumonia classification problem is cast into an anomaly detection to develop the aforementioned ML models. The experimental results show that pneumonia can be diagnosed with high recall, precision, -score, and -score using the proposed unsupervised models. In addition, we observe that the proposed models are competitive with the state-of-the-art models, which are trained on a labeled dataset.
世界人口在不断增长，现有医疗保健基础设施应对医疗诊断和评估不断增长的需求所面临的挑战也在增加。虽然人类专家主要负责诊断不同的医疗状况，但近年来人工智能（AI）辅助诊断已变得相当有用。肺炎是需要早期诊断和后续治疗以降低死亡率的关键肺部感染之一。有多种方法可用于获得肺炎诊断；然而，胸部X光的应用很普遍，因为它是非侵入性的。使用胸部X光进行肺炎诊断的AI系统通常基于监督机器学习（ML）模型构建，这些模型需要有标签的数据集来进行开发。然而，由于人力资源、成本和时间等限制，收集有标签的数据集有时是不可行的。因此，我们在本文中解决的问题是使用包括β-变分卷积自动编码器（β-VCAE）以及其他变体（如卷积自动编码器（CAE）、去噪卷积自动编码器（DCAE）和稀疏卷积自动编码器（SCAE））在内的无监督ML模型对肺炎进行无监督分类。也就是说，将肺炎分类问题转化为异常检测来开发上述ML模型。实验结果表明，使用所提出的无监督模型可以以高召回率、精确率、F1分数和AUC分数诊断肺炎。此外，我们观察到所提出的模型与在有标签数据集上训练的最先进模型具有竞争力。
