# Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

arXiv: https://arxiv.org/abs/2004.06578 · HTML: https://mlsanity.com/paper/2004.06578/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2312.07965/) (2023)
- [Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.06486/) (2021)
- [RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classification](https://mlsanity.com/paper/2509.08234/) (2025)
- [COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network](https://mlsanity.com/paper/2106.00436/) (2021)
- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data](https://mlsanity.com/paper/2206.07595/) (2022)
- [A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images](https://mlsanity.com/paper/2106.14207/) (2021)
- [A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia](https://mlsanity.com/paper/2212.14744/) (2022)
- [Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes](https://mlsanity.com/paper/2202.07361/) (2022)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)
- [CoVid-19 Detection leveraging Vision Transformers and Explainable AI](https://mlsanity.com/paper/2307.16033/) (2023)
- [Classification of Non-native Handwritten Characters Using Convolutional Neural Network](https://mlsanity.com/paper/2406.04511/) (2024)
- [Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis](https://mlsanity.com/paper/2501.10984/) (2025)
- [LightPneumoNet: Lightweight Pneumonia Classifier](https://mlsanity.com/paper/2510.11232/) (2025)
- [Enhanced Chest Disease Classification Using an Improved CheXNet Framework with EfficientNetV2-M and Optimization-Driven Learning](https://mlsanity.com/paper/2512.08992/) (2025)

## Abstract (arXiv)

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.