Pneumonia and COVID-19 Classification and Detection Based on Convolutional Neural Network: A Review

Abstract

Pneumonia diseases are considered one of the most pandemic diseases by the WHO, claiming the lives of millions across the world. Therefore, the necessity of having mechanisms for early diagnosis and detection of these epidemic diseases to preserve people's lives. On the other hand, the increase in cases requires not relying on traditional means of detecting diseases due to these tests' limitations and high costs like RT-PCR and errors in interpretation by humans. Among the available methods for diagnosing Pneumonia diseases are X-rays and CT scans. For accurate and highly efficient diagnosis, computer-aided diagnosis (CAD) is required. Deep learning has been suggested as the best solution to enhance the prognosis for many lung diseases using CAD systems. The ability of machine learning algorithms was not sufficient to diagnose with high accuracy due to several challenges, like: limited dataset, high computational, and less robustness because of huge numbers of features in the images. The aspiration to deep learning to solve the problems of diagnosis more accurate, especially convolution neural networks, showed impressive results in the classification of images. The convolutions layer in the network with filters automatically discover the critical spatial and temporal features in an image. Hierarchical representations were used in human brains in designing the learning process of CNN. Several deep learning architectures have been used to detect pneumonia diseases, the most popular such as AlexNet, VGG-16, Inception-v1, Inception-v3, ResNet-50, Inception-ResNet-V2, and ResNet201. The strength of CNNs lies in the size of data to be trained. Previous algorithms have been used to pre-train on a vast dataset. An augmentation strategy has been used in the model design to increase the dataset's size and quality artificially. This review aims to pave the way for better, more accurate, and efficient models for early detection and classification of lung diseases to improve patient survival.