Covid 19 diagnosis systems based on convolutional neural network techniques.

Image classification is a supervised machine learning that can be used to classify the images based on certain factors. Classification takes an image as input and then generates one or more labels that correspond to what it sees in the image. Image classification takes the image as input and interprets what it sees. The major goal of this work involves the use of Deep Learning (DL) approaches. This research work identifies COVID-19 dataset as a critical issue for medical practitioners nowadays because of its rapid dissemination. To address this issue, medical practitioners have tried a variety of tools and approaches to detect and restrict the spread of COVID-19. The best method for identifying COVID-19 is currently using CT (Computed Tomography) scan and X-ray imaging. This diagnostic procedure is particularly accurate since it can see organs in three dimensions. Unfortunately, this procedure requires a radiologist to detect the sickness and takes a long time, medical practitioners will lose crucial time. This research creates a method to detect the coronavirus automatically using DL algorithms. The purpose of this study is to give medical professionals a search tool for CT scans and X-ray images of lungs with COVID-19 infection. DL algorithms such as AlexNet, ResNet, Inception and VGG16 are addressed in this work that helps to automate the classification process. This research work incorporates the CT scans and X-ray images that helps to detect the COVID-19. Experimental result shows that VGG obtains good result compared to other algorithms. VGG obtains 94 % accuracy for X-ray images and 93% accuracy for CT-scan dataset and outperforms best result compared to other algorithms. [ABSTRACT FROM AUTHOR]