Abstract: H5 model in Convolutional Neural Networks (CNN) is a new innovation done by us. CNN (Convolutional Neural Network) is a popular NN algorithm and it clearly outperforms Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) in this project. Inception V3, ResNet50, MobileNet and Xception [1] are the existing CNN models but are found to be less accurate and more time consuming. In our R&D lab we have developed a new CNN model called the H5 model. It is the best fit after the output is obtained from Haar Cascade Classifiers. A model which was developed for facial detection and distinction is now used for all objects detection with more accuracy focusing on five regions with different pixel Intensity scheme. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities. In our previous paper on CNN we had exhibited one channel output. In this paper we are interested to know the performance of multichannel output with cascading.

Keywords: Multichannel output with cascading, 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 and one channel output.

PDF | DOI: 10.17148/IJARCCE.2021.10924

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