ItemConference Paper

High-precision lung disease detection and classification from chest radiographs using deep and ensemble neural networks

Title
High-precision lung disease detection and classification from chest radiographs using deep and ensemble neural networks
Creator
Veerabhaktula, Ashish Joseph; Sathiyamurthy, Babu Kumar; Kumar, Kukatlapalli Pradeep; Lingaraju, Anoop Ganadalu; Rangegowda, Nagarathna Chevvenahalli; Channegowda, Santhrupth Bundanoor
Description
Chest X-rays are a quick and effective way to diagnose lung diseases. This research developed deep learning models to automatically detect chest X-rays of COVID-19, normal, and viral pneumonia patients. The goal was to evaluate deep learning for automated detection of lung diseases from chest X-rays. The research implemented transfer learning with ResNet101 and EfficientNetB0 architectures using a public chest x-ray database with over 21,000 images across COVID-19, normal, and other pneumonia infection classes. Pretrained ImageNet weights were used to initialize the models before fine-tuning them to classify features in chest X-rays. Data augmentation techniques like rotation, shifting, and flipping were applied to expand the number and diversity of training images. The models achieved exceptional performance with accuracy scores of 93.7% for ResNet101 and 95.3% for EfficientNetB0 on test data. Additionally, an Ensemble model, the combination of the two models, was implemented, achieving an accuracy of 96.4%. The findings demonstrate the capability of Ensemble deep convolutional neural networks for accurate automated classification of chest X-rays for Lung disease. Through data augmentation and transfer learning, high-precision models were developed without needing exceedingly sizeable medical image datasets. These deep learning classifiers could serve as rapid diagnostic decision support systems to identify potential lung disease patients using readily available chest X-rays. Such tools could assist healthcare providers, especially when access to expensive diagnostic tests is limited. 2026 Author(s).
Source
AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20191;
Date
01-01-2026
Publisher
American Institute of Physics
Coverage
Veerabhaktula A.J., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Lingaraju A.G., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Rangegowda N.C., Department of Artificial Intelligence and Machine Learning, BNM Institute of Technology), Karnataka, Bengaluru, India; Channegowda S.B., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 0094243X;
Format
online
Language
English
Type
Conference paper

Citation

Veerabhaktula, Ashish Joseph; Sathiyamurthy, Babu Kumar; Kumar, Kukatlapalli Pradeep; Lingaraju, Anoop Ganadalu; Rangegowda, Nagarathna Chevvenahalli; Channegowda, Santhrupth Bundanoor, “High-precision lung disease detection and classification from chest radiographs using deep and ensemble neural networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25723.

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