Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2020-10-06 eCollection Date: 2020-01-01 DOI:10.1155/2020/8889023
Arun Sharma, Sheeba Rani, Dinesh Gupta
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Abstract

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

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基于人工智能的胸部 X 光图像分类,将其分为 COVID-19 和其他传染病。
冠状病毒病 2019(COVID-19)的持续大流行除了造成巨大的社会经济影响外,还引发了全球健康和医疗保健危机。这场危机的重大挑战之一是如何快速有效地识别和监测 COVID-19 患者,以便及时做出治疗、监测和管理决策。研究人员正在努力开发耗时较少的方法,以取代或补充基于 RT-PCR 的方法。本研究旨在利用胸部 X 光图像创建高效的深度学习模型,用于快速筛查 COVID-19 患者。我们使用公开的成人 COVID-19 患者的 PA 胸部 X 光图像来开发基于人工智能 (AI) 的 COVID-19 和其他主要传染病分类模型。为了扩大数据集规模并开发通用模型,我们对原始图像进行了 25 种不同类型的增强。此外,我们还利用迁移学习方法来训练和测试分类模型。两个表现最好的模型(每个模型都在旋转 120° 或 140° 角的 286 幅图像上进行了训练)的组合对正常图像、COVID-19 图像、非 COVID-19 图像、肺炎图像和肺结核图像的预测准确率最高。通过迁移学习方法训练的人工智能分类模型能有效地对代表所研究疾病的胸部 X 光图像进行分类。我们的方法比以前公布的方法更有效。这为基于人工智能的方法解决与 COVID-19 相关的生物医学成像分类问题迈出了一步。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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