Arivoli A, Golwala Devdatt, Reddy Rayirth
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Measur Sens. 2022 Oct;23:100392. doi: 10.1016/j.measen.2022.100392. Epub 2022 Aug 2.
COVID-19 continues to threaten the world with its impact and severity. This pandemic has created a sense of havoc and shook the world stretching the medical fraternity to an unimaginable extent, who are now facing fatigue and exhaustion. Due to the rapid increase in cases all across the globe demanding extensive medical care, people are hunting for resources like testing facilities, medical drugs and even hospital beds. Even people with mild to moderate infection are panicking and mentally giving up due to anxiety and desperation. To combat these issues, it is necessary to find an inexpensive and faster way to save lives and bring about a much-needed change. The most fundamental way through which this can be achieved is with the help of radiology which involves examination of Chest X rays. They are primarily used for the diagnosis of this disease. But due to panic and severity of this disease a recent trend of performing CT scans has been observed. This has been under scrutiny since it exposes patients to a very high level of radiation known to increase the probability of cancer. As quoted by the AIIMS Director, one CT scan is equivalent to around 300-400 Chest X-rays. Also, it is relatively a much costlier testing method. Hence, in this report, we have presented a Deep learning approach which can detect covid 19 positive cases from Chest X ray images. It involves creation of a Deep learning based Convolutional Neural Network (CNN) using Keras (python library) and integrating the model with a front-end user interface for ease of use. This leads up to the creation of a software which we have named as CoviExpert. It uses the sequential Keras model which is built layer by layer. All the layers are trained independently to make independent predictions which are then combined to give the final output. 1584 images of Chest X-rays of both COVID-19 positive and negative patients have been used as training data. 177 images have been used as testing data. The proposed approach gives a classification accuracy of 99%. CoviExpert can be used on any device by any medical professional to detect Covid positive patients within a few seconds.
新冠疫情仍在继续,其影响范围之广、危害程度之深,持续威胁着整个世界。这场大流行造成了一片混乱,震撼了全球,将医疗界推向了难以想象的极限,如今他们正面临着疲劳和精疲力竭的困境。由于全球范围内病例迅速增加,需要大量医疗护理,人们正在寻找检测设施、药品甚至医院床位等资源。即使是轻症至中症的感染者也因焦虑和绝望而恐慌,精神上濒临放弃。为了应对这些问题,有必要找到一种低成本且快速的方法来拯救生命,并带来急需的改变。实现这一目标最基本的方法是借助放射学,即通过胸部X光检查。胸部X光主要用于这种疾病的诊断。但由于这种疾病的恐慌性和严重性,最近出现了进行CT扫描的趋势。由于CT扫描会让患者暴露在已知会增加患癌几率的高水平辐射下,这一趋势一直受到审视。正如全印医学科学研究所所长所说,一次CT扫描相当于大约300 - 400次胸部X光检查。此外,它相对来说是一种成本更高的检测方法。因此,在本报告中,我们提出了一种深度学习方法,该方法可以从胸部X光图像中检测出新冠阳性病例。它涉及使用Keras(Python库)创建基于深度学习的卷积神经网络(CNN),并将该模型与前端用户界面集成,以便于使用。这促成了一款名为CoviExpert的软件的创建。它使用逐层构建的顺序Keras模型。所有层都独立训练以进行独立预测,然后将这些预测结果合并以给出最终输出。1584张新冠阳性和阴性患者的胸部X光图像被用作训练数据。177张图像被用作测试数据。所提出的方法分类准确率为99%。任何医疗专业人员都可以在任何设备上使用CoviExpert,在几秒钟内检测出新冠阳性患者。