# Deep Learning Models for Classification of COVID-19 Cases by Medical Images

arXiv: https://arxiv.org/abs/2310.16851 · HTML: https://mlsanity.com/paper/2310.16851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 72 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Analysis and Comparison of Classification Metrics](https://mlsanity.com/paper/2209.05355/) (2022)
- [CovidAID: COVID-19 Detection Using Chest X-Ray](https://mlsanity.com/paper/2004.09803/) (2020)

## Abstract (arXiv)

In recent times, the use of chest Computed Tomography (CT) images for detecting coronavirus infections has gained significant attention, owing to their ability to reveal bilateral changes in affected individuals. However, classifying patients from medical images presents a formidable challenge, particularly in identifying such bilateral changes. To tackle this challenge, our study harnesses the power of deep learning models for the precise classification of infected patients. Our research involves a comparative analysis of deep transfer learning-based classification models, including DenseNet201, GoogleNet, and AlexNet, against carefully chosen supervised learning models. Additionally, our work encompasses Covid-19 classification, which involves the identification and differentiation of medical images, such as X-rays and electrocardiograms, that exhibit telltale signs of Covid-19 infection. This comprehensive approach ensures that our models can handle a wide range of medical image types and effectively identify characteristic patterns indicative of Covid-19. By conducting meticulous research and employing advanced deep learning techniques, we have made significant strides in enhancing the accuracy and speed of Covid-19 diagnosis. Our results demonstrate the effectiveness of these models and their potential to make substantial contributions to the global effort to combat COVID-19.