AI & ML Group, IUST, Awantipora, India; Department of CSE, NIT, Srinagar, India.
AI & ML Group, IUST, Awantipora, India.
Radiography (Lond). 2021 May;27(2):483-489. doi: 10.1016/j.radi.2020.10.018. Epub 2020 Nov 11.
The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths and breadths of the globe, taking a death toll to nearly 700 k by the start of August 2020. The number is well expected to rise even more significantly. In the absence of a thoroughly tested and approved vaccine, the onus primarily lies on obliging to standard operating procedures and timely detection and isolation of the infected persons. The detection of SARS n-CoV2 has been one of the core concerns during the fight against this pandemic. To keep up with the scale of the outbreak, testing needs to be scaled at par with it. With the conventional PCR testing, most of the countries have struggled to minimize the gap between the scale of outbreak and scale of testing.
One way of expediting the scale of testing is to shift to a rigorous computational model driven by deep neural networks, as proposed here in this paper. The proposed model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost. The dataset used in this work contains 1428 chest radiographs with confirmed COVID-19 positive, common bacterial pneumonia, and healthy cases (no infection). We explored the pre-trained VGG-16 model for classification tasks in this. Transfer learning with fine-tuning was used in this study to train the network on relatively small chest radiographs effectively.
Initial experiments showed that the model achieved promising results and can be significantly used to expedite COVID-19 detection. The experimentation showed an accuracy of 96% and 92.5% in two and three output class cases, respectively.
We believe that this study could be used as an initial screening, which can help healthcare professionals to treat the COVID patients by timely detecting better and screening the presence of disease.
Its simplicity drives the proposed deep neural network model, the capability to work on small image dataset, the non-contact method with acceptable accuracy is a potential alternative for rapid COVID-19 testing that can be adapted by the medical fraternity considering the criticality of the time along with the magnitudes of the outbreak.
自 2019 年 12 月以来，一种由新发现的冠状病毒（命名为 SARS n-CoV2）引起的致命传染病的爆发一直没有放缓或停止的迹象。这种传染病已经蔓延到全球不同的地区和范围，截至 2020 年 8 月初，死亡人数已接近 70 万。预计这一数字还会显著上升。在没有经过彻底测试和批准的疫苗的情况下，主要责任在于遵守标准操作程序，及时发现和隔离感染者。SARS n-CoV2 的检测一直是抗击这一流行病的核心关注点之一。为了跟上疫情的规模，检测需要与之相匹配。在常规的 PCR 检测中，大多数国家都在努力缩小疫情规模和检测规模之间的差距。
加快检测规模的一种方法是转向基于深度神经网络的严格计算模型，本文即提出了这种方法。所提出的模型是一种非接触式的方法，用于确定受检者是否感染，它是通过使用胸部 X 光片来实现的；由于成像速度快、成本低，胸部 X 光片是临床诊断中最广泛使用的成像技术之一。本工作使用的数据集包含 1428 张胸部 X 光片，其中包括确诊的 COVID-19 阳性、常见细菌性肺炎和健康（无感染）病例。我们探索了在这个工作中使用预训练的 VGG-16 模型进行分类任务。在这个研究中使用了迁移学习和微调来有效地在相对较小的胸部 X 光片上训练网络。
初步实验表明，该模型取得了令人鼓舞的结果，可以显著用于加快 COVID-19 的检测。实验结果表明，在两种和三种输出类别的情况下，模型的准确率分别为 96%和 92.5%。
我们相信，这项研究可以作为一种初步的筛查手段，帮助医疗保健专业人员通过及时发现和筛查疾病来更好地治疗 COVID 患者。
该深度神经网络模型的简单性、在小型图像数据集上的工作能力、非接触方法和可接受的准确性，使其成为一种快速 COVID-19 检测的潜在替代方法，可以考虑到时间的紧迫性和疫情的规模，被医学界所采用。
