# Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2312.07965 · HTML: https://mlsanity.com/paper/2312.07965/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [LocalViT: Analyzing Locality in Vision Transformers](https://mlsanity.com/paper/2104.05707/) (2021)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [DenseNet: Implementing Efficient ConvNet Descriptor Pyramids](https://mlsanity.com/paper/1404.1869/) (2014)
- [Distant Domain Transfer Learning for Medical Imaging](https://mlsanity.com/paper/2012.06346/) (2020)
- [Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray](https://mlsanity.com/paper/2004.06578/) (2020)
- [Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks](https://mlsanity.com/paper/1909.11870/) (2019)
- [A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation](https://mlsanity.com/paper/2004.01317/) (2020)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites](https://mlsanity.com/paper/2312.14171/) (2023)

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

- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays](https://mlsanity.com/paper/2406.15117/) (2024)
- [Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization](https://mlsanity.com/paper/2508.04790/) (2025)
- [Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model](https://mlsanity.com/paper/2509.12287/) (2025)
- [MetaChest: Generalized few-shot learning of pathologies from chest X-rays](https://mlsanity.com/paper/2509.25590/) (2025)
- [Evaluating ChatGPT's Performance in Classifying Pneumonia from Chest X-Ray Images](https://mlsanity.com/paper/2510.21839/) (2025)
- [Melanoma Classification Through Deep Ensemble Learning and Explainable AI](https://mlsanity.com/paper/2511.00246/) (2025)
- [CGF-DETR: Cross-Gated Fusion DETR for Enhanced Pneumonia Detection in Chest X-rays](https://mlsanity.com/paper/2511.01730/) (2025)
- [CogEvo-Edu: Cognitive Evolution Educational Multi-Agent Collaborative System](https://mlsanity.com/paper/2512.00331/) (2025)

## Abstract (arXiv)

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. Therefore, this paper presents a computer-aided classification of pneumonia, coined as Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pre-trained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNN pre-trained (DenseNet169, MobileNetV2 and Vision Transformer) using the ImageNet database. Then, these models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art methods, and it obtains an accuracy of 93.91% and a F1-Score of 93.88% on the testing phase.