Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Abstract

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

COVID-19 detection using chest X-rays: is lung segmentation important for generalization?

“…In a previous study, we utilized a dataset that was very similar to our classification training database, we also trained dense neural networks (without segmentation), but we did not perform validation and testing on an external dataset (Bassi and Attux (2021b)). There, we could achieve accuracies above 90%, as is common in many COVID-19 detection studies, which also use internal validation, i.e., they randomly divide a single dataset in testing, validation and training (Shoeibi et al (2020)). Furthermore, in preliminary tests using the stacked DNN we proposed here, but without external validation, we could also achieve accuracies above 90%.…”

“…Deep neural networks (DNN) for COVID-19 detection were already proposed (Shoeibi et al (2020)). However, some researchers raised concerns about the possibility of bias.…”

“…This would be the best case scenario, as different classes would not present different biases. But COVID-19 classification datasets are generally relatively small and mixed, i.e., different classes have different sources, (Shoeibi et al (2020)). Our objective is to understand how bias affects a DNN classifying pneumonia, healthy individuals and COVID-19 in a dataset like this.…”

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

“…In recent years, various investigations have provided automated SZ diagnosis via EEG signals using Artificial Intelligence (AI) methods [18], [19], [20], [21]. The AI investigations in this field include conventional machine learning and DL methods [18], [19], [20], [21].…”

“…In recent years, various investigations have provided automated SZ diagnosis via EEG signals using Artificial Intelligence (AI) methods [18], [19], [20], [21]. The AI investigations in this field include conventional machine learning and DL methods [18], [19], [20], [21]. The AI-based SZ diagnosis algorithm includes preprocessing sections, features extraction and selection, and in the end, classification.…”