A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation

Abstract

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.

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

“…In order not to eliminate the effect of negative pixels as a result of the normalization process, the MVSR normalization technique is recommended. All detail about this technique is given reference [24].…”

Spike-Event X-ray Image Classification for 3D-NoC-Based Neuromorphic Pneumonia Detection

“…The authors first used a pre-trained ResNet18 to extract features from CXR images and then used a discrete social learning particle swarm optimization algorithm to select features and a support vector to classify the images. A novel normalization algorithm using a CNN was proposed in [54] and implemented on FPGA to facilitate the pre-assessment of COVID-19.…”

Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

“…In recent years, many studies in the field of biomedicine have focused on computer‐aided diagnostic systems to facilitate the detection of various diseases. Advances in artificial intelligence also show that deep learning architectures have the capacity to diagnose at the level of healthcare professionals 1–6 . Machine learning methods have been widely used in the field of medicine in recent years and many studies have been presented in the literature 7–9 …”