VER-Net: A Hybrid Transfer Learning Model for Lung Cancer Detection Using CT Scan Images.

Authors

Anindita Saha
Shahid Mohammad Ganie
Pijush Kanti Dutta Pramanik
Rakesh Kumar Yadav
Saurav Mallik
Zhongming Zhao

Publication Date

5-24-2024

Journal

BMC Medical Imaing

Abstract

BACKGROUND: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.

METHODS: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images.

RESULTS: The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy.

CONCLUSION: VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.

Keywords

Humans, Lung Neoplasms, Tomography, X-Ray Computed, Machine Learning, Deep Learning, Radiographic Image Interpretation, Computer-Assisted, Lung cancer detection, CT scan, Transfer learning, Image processing, Stacking, VGG19, EfficientNetB0, ResNet101

Comments

This article has been corrected. See BMC Med Imaging. 2024 May 31;24:128.

DOI

10.1186/s12880-024-01238-z

PMID

38789925

PMCID

PMC11127393

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes