# Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms

arXiv: https://arxiv.org/abs/2004.00038 · HTML: https://mlsanity.com/paper/2004.00038/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 24 of 24)

- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://mlsanity.com/paper/2102.06285/) (2021)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images](https://mlsanity.com/paper/2109.11480/) (2021)
- [Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images](https://mlsanity.com/paper/2206.01903/) (2022)
- [Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction](https://mlsanity.com/paper/2208.10937/) (2022)
- [Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities](https://mlsanity.com/paper/2109.00889/) (2021)
- [An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics](https://mlsanity.com/paper/2305.19016/) (2023)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification](https://mlsanity.com/paper/2312.12876/) (2023)
- [CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19](https://mlsanity.com/paper/2106.05861/) (2021)
- [WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays](https://mlsanity.com/paper/2107.01392/) (2021)
- [Understanding Human Innate Immune System Dependencies using Graph Neural Networks](https://mlsanity.com/paper/2108.02872/) (2021)
- [Res-Dense Net for 3D Covid Chest CT-scan classification](https://mlsanity.com/paper/2208.04613/) (2022)
- [Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images](https://mlsanity.com/paper/2212.13788/) (2022)
- [MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis](https://mlsanity.com/paper/2304.13135/) (2023)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)
- [Automatic Detection of COVID-19 from Chest X-ray Images Using Deep Learning Model](https://mlsanity.com/paper/2408.14927/) (2024)
- [Application of discrete Ricci curvature in pruning randomly wired neural networks: A case study with chest x-ray classification of COVID-19](https://mlsanity.com/paper/2509.05322/) (2025)

## Abstract (arXiv)

COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.