Effective detection of Covid-19 using Xception net architecture: A technical investigation using X-ray images.

IF 2.3 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-07-29 DOI:10.1177/14604582251363519
Kuljeet Singh, Surbhi Gupta, Neeraj Mohan, Sourabh Shastri, Sachin Kumar, Vibhakar Mansotra, Anurag Sinha, Saifullah Khalid
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引用次数: 0

Abstract

The disastrous era of COVID-19 has altered the perspectives of nearly all nations concerning the health and education sectors. Artificial intelligence is a pressing need that needs to be implemented thoroughly in the medical and educational fields. Imperatively, the diagnosis of Covid-19 has become crucial. In this study, we have designed a classification model based on Convolutional Neural Network (CNN) and transfer learning. The COVID-19 chest X-ray images have been considered for the proposed methodology and are classified as COVID-19 positive and normal cases. The proposed shallow CNN Model achieved an accuracy of 96%, which is computationally very effective as only three Convolutional blocks are required. Then, the Xception architecture-based model is experimented with. The accuracy and loss of the proposed model have been evaluated using Adam and SGD optimizer. With the Adam Optimizer, Xception Net achieved the best classification accuracy of 99.94%. The precision, recall, and f1-score of 100% are achieved. The proposed model has outperformed the previous studies in the same domain, which highlights the model's state-of-the-art performance. Our study will be helpful for decision-makers and can help further minimize mortality and morbidity by effectively diagnosing the disease.

使用异常网络架构有效检测Covid-19:使用x射线图像的技术调查。
COVID-19的灾难性时代几乎改变了所有国家对卫生和教育部门的看法。人工智能是迫切需要,需要在医疗和教育领域得到彻底实施。至关重要的是,Covid-19的诊断已变得至关重要。在本研究中,我们设计了一个基于卷积神经网络(CNN)和迁移学习的分类模型。已将COVID-19胸部x线图像纳入拟议方法,并将其分为COVID-19阳性病例和正常病例。所提出的浅层CNN模型的准确率达到96%,由于只需要三个卷积块,因此在计算上非常有效。然后,对基于异常体系结构的模型进行了实验。利用Adam和SGD优化器对模型的精度和损失进行了评估。使用Adam Optimizer, Xception Net达到了99.94%的最佳分类准确率。准确率、召回率和f1-score均达到100%。提出的模型在同一领域的表现优于以往的研究,这突出了模型的最先进的性能。我们的研究将有助于决策者,并可以通过有效的诊断进一步降低死亡率和发病率。
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
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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