Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.

IF 11.7 1区 工程技术 Q1 Engineering
N B Prakash, M Murugappan, G R Hemalakshmi, M Jayalakshmi, Mufti Mahmud
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引用次数: 24

Abstract

The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.

Abstract Image

Abstract Image

Abstract Image

基于超像素分割的COVID-19检测和感染定位深度迁移学习。
新型冠状病毒病(COVID-19)演变为大流行,每天造成数千人死亡,危及全球数百万人的生命。除了热扫描机制外,胸部影像学检查还为这种病毒的检测、感染的诊断和预后提供了有价值的见解。尽管胸部CT和胸部x线成像在COVID-19治疗的临床方案中很常见,但由于后者的图像采集程序简单,成像机制的可移动性,因此更受青睐。然而,与胸部CT图像相比,胸部x线图像在早期发现感染的敏感性较低。在本文中,我们提出了一个基于深度学习的框架来增强这些图像的诊断价值,以改善临床结果。它是传统的SqueezeNet分类器的一种变体,具有分割功能,该分类器使用从标准数据集的胸部x射线图像中提取的深度特征进行训练,用于二值和多类分类。二值分类器对COVID-19和非COVID-19图像的识别准确率达到99.53%。同样,多类分类器对COVID-19、病毒性肺炎和正常病例进行分类,准确率为99.79%。该模型被称为COVID-19超级像素挤压网(COVID-SSNet),对激活图进行超像素分割,提取带有感知图像特征的感兴趣区域,并用这些区域构建胸部x射线图像的叠加。所提出的分类器模型为胸部x射线增加了重要价值,可以对影响分类器决策的图像特征和图像区域进行整体检查,从而加快COVID-19治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society CONSTRUCTION & BUILDING TECHNOLOGYGREEN &-GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
CiteScore
18.40
自引率
13.70%
发文量
810
期刊介绍: Sustainable Cities and Society (SCS) is an international journal focusing on fundamental and applied research aimed at designing, understanding, and promoting environmentally sustainable and socially resilient cities.
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