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

arXiv: https://arxiv.org/abs/2007.10785 · HTML: https://mlsanity.com/paper/2007.10785/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment](https://mlsanity.com/paper/2210.13005/) (2022)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [COVID-19 detection using chest X-rays: is lung segmentation important for generalization?](https://mlsanity.com/paper/2104.06176/) (2021)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection](https://mlsanity.com/paper/2209.04445/) (2022)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images](https://mlsanity.com/paper/2110.14013/) (2021)
- [A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans](https://mlsanity.com/paper/2208.02910/) (2022)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression](https://mlsanity.com/paper/2109.05461/) (2021)
- [Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency](https://mlsanity.com/paper/2203.14616/) (2022)
- [Faster ISNet for Background Bias Mitigation on Deep Neural Networks](https://mlsanity.com/paper/2401.08409/) (2024)
- [MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI](https://mlsanity.com/paper/2204.06681/) (2022)
- [A review of Deep learning Techniques for COVID-19 identification on Chest CT images](https://mlsanity.com/paper/2208.00032/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study](https://mlsanity.com/paper/2212.08798/) (2022)
- [Operational Collective Intelligence of Humans and Machines](https://mlsanity.com/paper/2402.13273/) (2024)
- [Pneumonia App: a mobile application for efficient pediatric pneumonia diagnosis using explainable convolutional neural networks (CNN)](https://mlsanity.com/paper/2404.00549/) (2024)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)

## Abstract (arXiv)

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, and also 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 and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, 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 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 is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed.