Improving Disease Classification Accuracy with Hybrid CNN-RNN Architectures for Lung Tumors

Vishal Patil, Vineet S Hiremani, Adil Mulimani, Shreeniwas R Kolagal, Channabasappa Muttal

2025

Abstract

The detection of lung nodules is essential in medical imaging, playing a critical role in diagnosing lung cancer at its early stages and supporting timely treatment. This study introduces a hybrid CNN-RNN model designed to enhance the accuracy and precision of lung nodule identification in computed tomography (CT) scans. The framework combines the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) with the temporal sequence analysis strengths of Recurrent Neural Networks (RNNs), effectively integrating spatial and temporal information for improved detection performance. Trained on a labeled dataset of CT images, the model’s performance was assessed using metrics such as precision, recall, F1 score, and area under the curve (AUC). The proposed method surpassed existing techniques, achieving an accuracy of 96.1%, an F1 score of 0.8434, an AUC of 0.901, a precision of 76.02%, and a recall of 94.81%. It demonstrated significant advancements over hybrid CNN-LSTM models previously used in related fields like Parkinson’s disease detection, agricultural disease analysis, and lung cancer prognosis estimation, which recorded lower precision, recall, and F1 scores. These findings highlight the potential of CNN-RNN architectures for lung nodule detection and their promise in advancing early lung cancer diagnosis.

Paper Citation

in Harvard Style

Patil V., Hiremani V., Mulimani A., Kolagal S. and Muttal C. (2025). Improving Disease Classification Accuracy with Hybrid CNN-RNN Architectures for Lung Tumors. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 574-580. DOI: 10.5220/0013597000004664

in Bibtex Style

@conference{incoft25,
author={Vishal Patil and Vineet Hiremani and Adil Mulimani and Shreeniwas Kolagal and Channabasappa Muttal},
title={Improving Disease Classification Accuracy with Hybrid CNN-RNN Architectures for Lung Tumors},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={574-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013597000004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Improving Disease Classification Accuracy with Hybrid CNN-RNN Architectures for Lung Tumors
SN - 978-989-758-763-4
AU - Patil V.
AU - Hiremani V.
AU - Mulimani A.
AU - Kolagal S.
AU - Muttal C.
PY - 2025
SP - 574
EP - 580
DO - 10.5220/0013597000004664
PB - SciTePress