Wavelet Based CNN for Diagnosis of COVID 19 using Chest X Ray

Abstract

Human race has overcome numerous pandemic and epidemics like Spanish flu, SARS, cholera, plague, etc since ages and COVID 19 pandemic is one among them. COVID 19 being the recent one, is much different than the others due to the contribution of AI in diagnosis and prediction of COVID 19 patients. Among the various use cases, one widely used area is medical diagnosis. AI and deep learning based algorithms are exploited enormously by data scientist to support radiologist in early prediction and detection of corona patients. Subsequently, in this work, we utilize wavelet based Convolutional Neural Networks for detecting and recognizing of COVID 19 cases from chest X ray images. Currently, previous works utilize existing CNN architectures for classification of healthy and affected chest X rays, however these networks process the image in a single resolution and loss the potential features present in other resolutions of the input image. Wavelets are known to decompose the image into different spatial resolutions based on the high pass and low pass frequency components and extract valuable features from the affected portion of lung X ray efficiently. Henceforth, in this article, we utilize a hybrid CNN model of wavelet and CNN to diagnose the lung X rays. The proposed CNN model is trained and validated on open source COVID 19 chest X ray images and performs better than existing state of the art CNN models with an accuracy of 99.25%, ROC-AUC value of 1.00 and very less false negative values. Further, the performance of our model is validated by Gradient Class Activation Map visualization technique. The visualization of feature maps clearly indicates that our proposed network has perfectly extracted features from the corona virus affected portion of the lung.