# Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning

arXiv: https://arxiv.org/abs/2104.05121 · HTML: https://mlsanity.com/paper/2104.05121/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images](https://mlsanity.com/paper/2004.02696/) (2020)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans](https://mlsanity.com/paper/2010.16041/) (2020)
- [Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification](https://mlsanity.com/paper/2003.09860/) (2020)
- [COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning](https://mlsanity.com/paper/2009.14623/) (2020)
- [MIXCAPS: A Capsule Network-based Mixture of Experts for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2008.06072/) (2020)

## Built on by (most cited first, 8 of 8)

- [MEDFAIR: Benchmarking Fairness for Medical Imaging](https://mlsanity.com/paper/2210.01725/) (2022)
- [A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images](https://mlsanity.com/paper/2106.14403/) (2021)
- [Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset](https://mlsanity.com/paper/2108.07148/) (2021)
- [BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift](https://mlsanity.com/paper/2304.01838/) (2023)
- [Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans](https://mlsanity.com/paper/2109.09241/) (2021)
- [PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis](https://mlsanity.com/paper/2206.15069/) (2022)
- [Enhancing COVID-19 Severity Analysis through Ensemble Methods](https://mlsanity.com/paper/2303.07130/) (2023)
- [COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-Rendered Computer Tomography](https://mlsanity.com/paper/2308.01433/) (2023)

## Abstract (arXiv)

We propose a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community-Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images. In the first stage, an infection - COVID-19 or CAP, is detected using a pre-trained DenseNet architecture. Then, in the second stage, a fine-grained three-way classification is done using EfficientNet architecture. The proposed COVID+CAP-CNN framework achieved a slice-level classification accuracy of over 94% at identifying COVID-19 and CAP. Further, the proposed framework has the potential to be an initial screening tool for differential diagnosis of COVID-19 and CAP, achieving a validation accuracy of over 89.3% at the finer three-way COVID-19, CAP, and healthy classification. Within the IEEE ICASSP 2021 Signal Processing Grand Challenge (SPGC) on COVID-19 Diagnosis, our proposed two-stage classification framework achieved an overall accuracy of 90% and sensitivity of .857, .9, and .942 at distinguishing COVID-19, CAP, and normal individuals respectively, to rank first in the evaluation. Code and model weights are available at https://github.com/shubhamchaudhary2015/ct_covid19_cap_cnn