Yaman Sertaç, Karakaya Barış, Erol Yavuz
Faculty of Engineering, Department of Electrical-Electronics Engineering, Hakkari University, 30000 Hakkari, Turkey.
Faculty of Engineering, Department of Electrical-Electronics Engineering, Firat University, 23119 Elazığ, Turkey.
Evol Syst (Berl). 2022 Feb 1:1-11. doi: 10.1007/s12530-022-09419-3.
COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean-Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.
新冠病毒病仍然是一种致命疾病，它通过影响人类肺部威胁着所有人。胸部X光或计算机断层扫描成像通常用于进行快速可靠的医学检查以检测新冠病毒。这些医学图像极具挑战性，因为这是一项需要全身心投入的工作且容易出现人为错误。本文提出了一种新的归一化算法，该算法分别由均值 - 方差 - 软最大 - 重缩放（MVSR）过程组成，以促进对新冠病毒病的预评估和诊断。为了展示MVSR归一化技术的效果，将所提方法的算法应用于胸部X光和新冠病毒计算机断层扫描图像数据集。使用MVSR归一化后的X光图像通过卷积神经网络（CNN）模型来识别新冠病毒。在实现阶段，MVSR算法在MATLAB环境中执行，然后借助FPGA平台上的定点分数表示格式，将MVSR归一化的所有算术运算用VHDL编码。实验平台由Zynq - 7000开发FPGA板和VGA显示器组成，用于显示原始的和经过MVSR归一化的胸部X光图像。CNN模型使用带有Python语言的Anaconda Navigator接口构建并执行。基于本研究结果，使用MVSR归一化技术可以轻松诊断新冠病毒病感染情况。对于胸部X光图像的二分类，所提的MVSR归一化技术将CNN模型的分类准确率从83.01%提高到了96.16%。
