A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.
A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.
复制标题
DOI：
10.3390/healthcare9050522
复制
发表时间：
2021-04-29
期刊：
影响因子：
--
通讯作者：
Rahman S
中科院分区：
文献类型：
--
作者：
Almalki YE;Qayyum A;Irfan M;Haider N;Glowacz A;Alshehri FM;Alduraibi SK;Alshamrani K;Alkhalik Basha MA;Alduraibi A;Saeed MK;Rahman S
2019 年冠状病毒病（COVID-19）是一种在世界范围内迅速且无法控制地传播的传染病。关键的挑战是快速检测冠状病毒感染者。正在使用的技术包括体温测量以及前鼻拭子分析。然而，采集鼻拭子和实验室检测是复杂的、侵入性的，并且需要许多资源。此外，缺乏应对超标病例的检测试剂盒也是一个主要限制。当前的挑战是开发一些技术，通过深度学习（DL）等人工智能（AI）技术，非侵入性地检测疑似冠状病毒患者。开展该领域研究的另一个挑战是，由于同意参与研究的患者数量有限，因此难以获取数据集。纵观人工智能在医疗保健系统中的功效，研究人员开发一种人工智能算法来帮助卫生专业人员和政府官员自动识别和隔离有冠状病毒症状的人是一个巨大的挑战。因此，本文提出了一种新方法 CoVIRNet（COVID Inception-ResNet 模型），利用胸部 X 光自动诊断 COVID-19 患者。所提出的算法具有不同的初始残差块，通过使用不同尺度、不同层的不同深度特征图来迎合信息。使用平均池层将特征连接到每个建议的分类块，并将连接的特征传递到全连接层。所提出的高效深度学习模块使用不同的正则化技术来最大限度地减少由于 COVID-19 数据集较小而导致的过度拟合。在所提出的深度学习模型的不同级别提取多尺度特征，然后嵌入到各种机器学习模型中，以验证深度学习和机器学习模型的结合。与现有最先进的深度学习方法相比，所提出的 CoVIR-Net 模型达到了 95.7% 的准确率，而带有随机森林分类器的 CoVIR-Net 特征提取器的准确率达到了 97.29%，这是最高的。所提出的模型将成为用于评估和分类 COVID-19 的自动解决方案。我们预测，与当前使用的最先进技术相比，所提出的方法将表现出出色的性能。
The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.
登录
查看更多内容
影响因子：
3.9
作者：
Das, Amit Kumar;Ghosh, Sayantani;Chakrabarti, Amlan
通讯作者：
Chakrabarti, Amlan
DOI：
10.3390/ijerph18063056
发表时间：
2021-03-16
影响因子：
--
作者：
Irfan M;Iftikhar MA;Yasin S;Draz U;Ali T;Hussain S;Bukhari S;Alwadie AS;Rahman S;Glowacz A;Althobiani F
通讯作者：
Althobiani F
DOI：
10.3390/s21072435
发表时间：
2021-04-01
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Fenga L;Gaspari M
通讯作者：
Gaspari M
基于AI的端到端护理诊断系统，用于分类呼吸系统疾病和早期发现Covid-19：一个理论框架。
影响因子：
3.9
作者：
Belkacem AN;Ouhbi S;Lakas A;Benkhelifa E;Chen C
通讯作者：
Chen C
影响因子：
6.1
作者：
Khan, Asif Iqbal;Shah, Junaid Latief;Bhat, Mohammad Mudasir
通讯作者：
Bhat, Mohammad Mudasir