# Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

arXiv: https://arxiv.org/abs/2003.14363 · HTML: https://mlsanity.com/paper/2003.14363/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images](https://mlsanity.com/paper/2202.07118/) (2022)
- [Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM](https://mlsanity.com/paper/2206.04548/) (2022)
- [CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis](https://mlsanity.com/paper/2311.14462/) (2023)
- [Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images](https://mlsanity.com/paper/2501.16249/) (2025)
- [Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features](https://mlsanity.com/paper/2101.07866/) (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)
- [A distillation based approach for the diagnosis of diseases](https://mlsanity.com/paper/2108.03470/) (2021)
- [Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review](https://mlsanity.com/paper/2303.16754/) (2023)

## Abstract (arXiv)

Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).