Masud Mehedi
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.
Multimed Syst. 2022;28(4):1165-1174. doi: 10.1007/s00530-021-00857-8. Epub 2022 Jan 7.
The COVID-19 pandemic has opened numerous challenges for scientists to use massive data to develop an automatic diagnostic tool for COVID-19. Since the outbreak in January 2020, COVID-19 has caused a substantial destructive impact on society and human life. Numerous studies have been conducted in search of a suitable solution to test COVID-19. Artificial intelligence (AI) based research is not behind in this race, and many AI-based models have been proposed. This paper proposes a lightweight convolutional neural network (CNN) model to classify COVID and Non_COVID patients by analyzing the hidden features in the X-Ray images. The model has been evaluated with different standard metrics to prove the reliability of the model. The model obtained 98.78%, 93.22%, and 92.7% accuracy in the training, validation, and testing phases. In addition, the model achieved 0.964 scores in the Area Under Curve (AUC) metric. We compared the model with four state-of-art pre-trained models (VGG16, InceptionV3, DenseNet121, and EfficientNetB6). The evaluation results demonstrate that the proposed CNN model is a candidate for an automatic diagnostic tool for the classification of COVID-19 patients using chest X-ray images. This research proposes a technique to classify COVID-19 patients and does not claim any medical diagnosis accuracy.
新冠疫情给科学家们带来了诸多挑战，要求他们利用海量数据开发一种针对新冠病毒的自动诊断工具。自2020年1月疫情爆发以来，新冠病毒对社会和人类生活造成了巨大的破坏影响。人们开展了大量研究以寻找检测新冠病毒的合适解决方案。基于人工智能（AI）的研究在这场竞赛中也不落后，已经提出了许多基于AI的模型。本文提出了一种轻量级卷积神经网络（CNN）模型，通过分析X光图像中的隐藏特征来对新冠患者和非新冠患者进行分类。该模型已使用不同的标准指标进行评估，以证明其可靠性。该模型在训练、验证和测试阶段分别获得了98.78%、93.22%和92.7%的准确率。此外，该模型在曲线下面积（AUC）指标上取得了0.964的分数。我们将该模型与四个最先进的预训练模型（VGG16、InceptionV3、DenseNet121和EfficientNetB6）进行了比较。评估结果表明，所提出的CNN模型是一种利用胸部X光图像对新冠患者进行分类的自动诊断工具的候选模型。本研究提出了一种对新冠患者进行分类的技术，但并不声称具有任何医学诊断准确性。
