COVID-19 Diagnosis and Detection Based on Deep Learning Models

Abstract

The COVID-19 pandemic has caused widespread illness and death since its emergence in 2019. This study examines various Artificial Intelligence (AI) techniques for diagnosing and predicting COVID-19. One such method is the Hust 19 model, which employs a hybrid learning architecture of CNN and DNN models. The model divides CT scans into three types that identify COVID-19-related imaging features and implements a deep learning framework based on the VGG16 architecture. Another group of researchers developed a deep learning-based COVID-19 diagnostic system using multi-class and multi-center data, segmenting lungs and identifying COVID-19 infection slices. They evaluated the model's accuracy using Receiver Operating Characteristic (ROC) curves. A third group developed a deep neural network based on DenseNet121, standardizing input CXR images through anatomical landmark detection and registration, and segmenting lung lesions to diagnose pneumonia. A final group developed a three-dimensional deep learning model called COVNet, which takes CT images as input, extracts features from each slice using the ResNet50 backbone, merges the maximum features obtained by the AI model, and generates classification predictions for the entire CT scan. They also proposed a multi-decoder split network to improve the model's accuracy and efficiency. Experimental results show that the Deep learning AI system model and COVNet model are relatively good, with average sensitivity and specificity. The remaining models, particularly Hust-19, show prominent specificity, but high specificity leads to low sensitivity, making the overall model imbalanced. These AI diagnostic models are just the beginning, and there may be more inventions and creations in the future.