Early Detection of Pneumonia in COVID-19 Patients Using CNN Algorithm

Abstract: Neural Networks (NN) are a subset of Machine Learning that is increasingly being employed in pre-processed image analysis. The CNN (Convolutional Neural Network) algorithm is a common NN technique that outperforms ANN in this project. The existing CNN models are Inception V3, ResNet50, MobileNet, and Xception [1], although they have been proven to be less accurate and time expensive. The H5 model is a new CNN model developed in our Project. A model that was originally created for facial detection and differentiation is currently being utilised to detect all objects with greater accuracy, focusing on five zones with variable pixel intensity scheme. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate Pneumonia Detection in COVID-19 patients from radiograph images to provide a fast and reliable evidence of Pneumonia related COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.

Keywords: H5 Convolutional Neural Network model, Convolution Neural Network (CNN) architecture, COVID-19, Severe Acute Respiratory Syndrome corona virus 2 (SARS cov-2), deep learning based chest radiograph classification (DL-CRC), Tensorflow, Haar Cascade Classifiers, different pixel Intensity scheme, facial detection and distinction.