Deep Learning for COVID-19: COVID-19 Detection Based on Chest X-Ray Images by the Fusion of Deep Learning and Machine Learning Techniques

Abstract

The proposed book chapter focuses on development of an efficient AI based medical imaging solution for COVID-19 by leveraging the easily available COVID X-Ray Images (CXR). For this the experimentation with different deep learning and machine learning algorithms is performed. A convolution network (CNN) is one of the widely used deep learning algorithms used for medical imaging systems. In this chapter different variants of CNN are used. Data augmentation and dropout techniques are used to avoid overfitting. Among these different variants, CNN with ten convolutional layers and 15 epochs has given the best performance of 88.23% training 85.94% validation accuracy. This is followed by the use of Alexnet for feature extraction from CXR images and the extracted features are given as the input to different machine learning classifiers including Gaussian Naïve Bays, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boost, Ada Boost for classification of input images among the classes of Covid-19, No Finding, Pneumonia. Among these machine learning classifiers, SVM has given the best performance of 86% testing accuracy. Thus, the deep learning algorithms have proven to give satisfactory performance. This performance can still be improved with the help of more images and by fine tuning the pre trained models. In addition, this chapter highlights the importance of transfer learning, brief description about medical imaging.