ItemArticle

Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology

Title
Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology
Creator
Natarajan, Jayapandian; Moozhippurath, Bineesh
Description
Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35, surpassing single-model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use. This is an open access article under the CC BY-SA license.
Source
IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1605-1612
Date
01-01-2026
Publisher
Institute of Advanced Engineering and Science
Subject
Deep learning; Ensemble learning; Generative adversarial networks; Lung cancer detection; Medical imaging; Thoracic computed tomography
Coverage
Natarajan J., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Moozhippurath B., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India, Department of Artificial Intelligence and Data Science, Jyothi Engineering College, Thrissur, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20894872;
Format
online
Language
English
Type
Article

Citation

Natarajan, Jayapandian; Moozhippurath, Bineesh, “Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23082.

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