LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection

Premananda Sahu; Ashwani Kumar; Mahesh K. Singh; Rituraj Jain; Kamal Upreti; Jyoti Parashar

doi:10.35377/saucis...1665478

EN

LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection

Abstract

Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications.

Keywords

References

B. Ozdemir, E. Aslan, and I. Pacal, "Attention enhanced inceptionNext-based hybrid deep learning model for lung cancer detection," IEEE Access, vol. 13, pp. 27050–27069, 2025, doi: 10.1109/ACCESS.2025.3539122.
H. Rajaguru and K. Shanmugam, "Enhanced superpixel guided ResNet framework with optimized deep weighted averaging based feature fusion for lung cancer detection in histopathological images," Preprints, Feb. 2025, doi: 10.20944/preprints202502.0736.v1.
M. Reck, S. Dettmer, H.-U. Kauczor, R. Kaaks, N. Reinmuth, and J. Vogel-Claussen, "Lung cancer screening with low-dose computed tomography: Current status in Germany," Dtsch. Arztebl. Int., Jun. 2023, doi: 10.3238/arztebl.m2023.0099.
A. Schreuder, E. T. Scholten, B. van Ginneken, and C. Jacobs, "Artificial intelligence for detection and characterisation of pulmonary nodules in lung cancer CT screening: Ready for practice?," Transl. Lung Cancer Res., vol. 10, no. 5, pp. 2378–2388, May 2021, doi: 10.21037/tlcr-2020-lcs-06.
A. K. Esim, H. Kaya, and V. Alcan, "Determination of malignant melanoma by analysis of variation values," Turkish J. Eng., vol. 3, no. 3, pp. 120–126, Jul. 2019, doi: 10.31127/tuje.472328.
M. Dirik, "Machine learning-based lung cancer diagnosis," Turkish J. Eng., vol. 7, no. 4, pp. 322–330, Oct. 2023, doi: 10.31127/tuje.1180931.
S. N. Polater and O. Sevli, "Deep learning based classification for alzheimer's disease detection using MRI images," Turkish J. Eng., vol. 8, no. 4, pp. 729–740, Oct. 2024, doi: 10.31127/tuje.1434866.
D. Maza, J. O. Ojo, and G. O. Akinlade, "A predictive machine learning framework for diabetes," Turkish J. Eng., vol. 8, no. 3, pp. 583–592, Jul. 2024, doi: 10.31127/tuje.1434305.

P. Kaur et al., "DELM: Deep ensemble learning model for multiclass classification of super-resolution leaf disease images," Turkish J. Agric. For., vol. 47, no. 5, pp. 727–745, Oct. 2023, doi: 10.55730/1300-011X.3123.
K. Meghraoui, I. Sebari, S. Bensiali, and K. A. El Kadi, "On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco," Adv. Eng. Sci., vol. 2, pp. 118–126, 2022.
H. Ghayoomi and M. Partohaghighi, "Investigating lake drought prevention using a DRL-based method," Eng. Appl., vol. 2, no. 1, pp. 49–59, 2023.
H. F. Kayıran, "The function of artificial intelligence and its sub-branches in the field of health," Eng. Appl., vol. 1, no. 2, pp. 99–107, 2022.
E. O. Nwafor and F. O. Akintayo, "Predicting trip purposes of households in Makurdi using machine learning: A comparative analysis of decision tree, CatBoost, and XGBoost algorithms," Eng. Appl., vol. 3, no. 3, pp. 260–274, 2024.
M. Cellina et al., "Artificial intelligence in lung cancer screening: The future is now," Cancers, vol. 15, no. 17, p. 4344, Aug. 2023, doi: 10.3390/cancers15174344.
R. T. Sadia, J. Chen, and J. Zhang, "CT image denoising methods for image quality improvement and radiation dose reduction," J. Appl. Clin. Med. Phys., vol. 25, no. 2, Feb. 2024, doi: 10.1002/acm2.14270.
R. R. Shivwanshi and N. Nirala, "Hyperparameter optimisation and development of an advanced CNN-based technique for lung nodule assessment," Phys. Med. Biol., vol. 68, no. 17, p. 175038, Sep. 2023, doi: 10.1088/1361-6560/acef8c.
P. Sathe, A. Mahajan, D. Patkar, and M. Verma, "End-to-end fully automated lung cancer screening system," IEEE Access, vol. 12, pp. 108515–108532, 2024, doi: 10.1109/ACCESS.2024.3435774.
N. Gautam, A. Basu, and R. Sarkar, "Lung cancer detection from thoracic CT scans using an ensemble of deep learning models," Neural Comput. Appl., vol. 36, no. 5, pp. 2459–2477, Feb. 2024, doi: 10.1007/s00521-023-09130-7.
H. Al Ewaidat and Y. El Brag, "Identification of lung nodules CT scan using YOLOv5 based on convolution neural network," 2022.
P. G. Mikhael et al., "Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography," J. Clin. Oncol., vol. 41, no. 12, pp. 2191–2200, Apr. 2023, doi: 10.1200/JCO.22.01345.
J. L. Causey et al., "Spatial pyramid pooling with 3D convolution improves lung cancer detection," IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 19, no. 2, pp. 1165–1172, Mar. 2022, doi: 10.1109/TCBB.2020.3027744.
A. Elnakib, H. M. Amer, and F. E. Z. Abou-Chadi, "Early lung cancer detection using deep learning optimisation," Int. J. Online Biomed. Eng., vol. 16, no. 06, pp. 82–94, May 2020, doi: 10.3991/ijoe.v16i06.13657.
S.-C. Hung, Y.-T. Wang, and M.-H. Tseng, "An interpretable three-dimensional artificial intelligence model for computer-aided diagnosis of lung nodules in computed tomography images," Cancers, vol. 15, no. 18, p. 4655, Sep. 2023, doi: 10.3390/cancers15184655.
C. Jacobs et al., "Deep learning for lung cancer detection on screening CT scans: Results of a large-scale public competition and an observer study with 11 radiologists," Radiol. Artif. Intell., vol. 3, no. 6, Nov. 2021, doi: 10.1148/ryai.2021210027.
Y. Wang et al., "Leveraging serial low-dose CT scans in radiomics-based reinforcement learning to improve early diagnosis of lung cancer at baseline screening," Radiol. Cardiothorac. Imaging, vol. 6, no. 3, Jun. 2024, doi: 10.1148/ryct.230196.
A. Saha, S. M. Ganie, P. K. D. Pramanik, R. K. Yadav, S. Mallik, and Z. Zhao, "VER-Net: A hybrid transfer learning model for lung cancer detection using CT scan images," BMC Med. Imaging, vol. 24, no. 1, p. 120, May 2024, doi: 10.1186/s12880-024-01238-z.
L. Song, M. Zhang, and L. Wu, "Detection of low dose CT pulmonary nodules based on 3D CNN-CapsNet," Jun. 2023, doi: 10.22541/au.168576934.49766817/v1.
J. Shao et al., "Deep learning empowers lung cancer screening based on mobile low-dose computed tomography in resource-constrained sites," Front. Biosci. Landmark, vol. 27, no. 7, Jul. 2022, doi: 10.31083/j.fbl2707212.
R. Anand, "Lung cancer detection and prediction using deep learning," Int. J. Eng. Appl. Sci. Technol., vol. 7, no. 1, pp. 313–320, May 2022, doi: 10.33564/IJEAST.2022.v07i01.048.
A. R. Wahab Sait, "Lung cancer detection model using deep learning technique," Appl. Sci., vol. 13, no. 22, p. 12510, Nov. 2023, doi: 10.3390/app132212510.
K. Ahmed, S. S. Ahmed, A. Talukdar, and D. Chakrabarty, "An empirical study on lung cancer detection and classification using machine learning and image processing techniques," in Adv. Intell. Syst. Comput., 2024, pp. 165–176, doi: 10.1007/978-3-031-75771-6_11.
B. Lee et al., "Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer," Sens. Actuators B Chem., vol. 409, p. 135578, Jun. 2024, doi: 10.1016/j.snb.2024.135578.
X. Yang, T. Ye, Q. Wang, and Z. Tao, "Diagnosis of blade icing using multiple intelligent algorithms," Energies, vol. 13, no. 11, p. 2975, Jun. 2020, doi: 10.3390/en13112975.
U. Prasad, S. Chakravarty, and G. Mahto, "Lung cancer detection and classification using deep neural network based on hybrid metaheuristic algorithm," Soft Comput., vol. 28, no. 15–16, pp. 8579–8602, Aug. 2024, doi: 10.1007/s00500-023-08845-y.
N. Venkatesan, S. Pasupathy, and B. Gobinathan, "An efficient lung cancer detection using optimal SVM and improved weight based beetle swarm optimisation," Biomed. Signal Process. Control, vol. 88, p. 105373, Feb. 2024, doi: 10.1016/j.bspc.2023.105373.
H. Liz-López, Á. A. de Sojo-Hernández, S. D'Antonio-Maceiras, M. A. Díaz-Martínez, and D. Camacho, "Deep learning innovations in the detection of lung cancer: Advances, trends, and open challenges," Cognit. Comput., vol. 17, no. 2, p. 67, Apr. 2025, doi: 10.1007/s12559-025-10408-2.
P. Sahu, B. Kumar Sahoo, S. Kumar Mohapatra, and P. Kumar Sarangi, "Segmentation of encephalon tumor by applying soft computing methodologies from magnetic resonance images," Mater. Today Proc., vol. 80, pp. 3371–3375, 2023, doi: 10.1016/j.matpr.2021.07.255.
P. Sahu, P. K. Sarangi, S. K. Mohapatra, and B. K. Sahoo, "Detection and classification of encephalon tumor using extreme learning machine learning algorithm based on deep learning method," in Smart Innov. Syst. Technol., 2022, pp. 285–295, doi: 10.1007/978-981-16-8739-6_26.
P. Sahu, S. Kumar Mohapatra, U. Punia, P. Kumar Sarangi, J. Mohanty, and M. Rohra, "Deep learning techniques based brain tumor detection," in Proc. 11th Int. Conf. Reliab. Infocom Technol. Optim. (ICRITO), Mar. 2024, pp. 1–5, doi: 10.1109/ICRITO61523.2024.10522358.
H. Dawood, M. Nawaz, M. U. Ilyas, T. Nazir, and A. Javed, "Attention-guided CenterNet deep learning approach for lung cancer detection," Comput. Biol. Med., vol. 186, p. 109613, Mar. 2025, doi: 10.1016/j.compbiomed.2024.109613.
A. Priya and P. Shyamala Bharathi, "SE-ResNeXt-50-CNN: A deep learning model for lung cancer classification," Appl. Soft Comput., vol. 171, p. 112696, Mar. 2025, doi: 10.1016/j.asoc.2025.112696.
N. Aydin Atasoy and A. Faris Abdulla Al Rahhawi, "Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset," Int. J. Imaging Syst. Technol., vol. 34, no. 3, May 2024, doi: 10.1002/ima.23067.

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Premananda Sahu
0000-0002-9360-8423
India

Ashwani Kumar
0000-0002-2100-900X
India

Mahesh K. Singh
0009-0006-5036-6037
India

Rituraj Jain
0000-0002-5532-1245
India

Kamal Upreti ^*
0000-0003-0665-530X
India

Jyoti Parashar
0000-0002-6573-4270
India

Early Pub Date

June 13, 2025

Publication Date

June 30, 2025

Submission Date

March 25, 2025

Acceptance Date

April 21, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1665478

IZ

https://izlik.org/JA39JD87DX

Cite

RIS / Bibtex

APA

Sahu, P., Kumar, A., Singh, M. K., Jain, R., Upreti, K., & Parashar, J. (2025). LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection. Sakarya University Journal of Computer and Information Sciences, 8(2), 184-197. https://doi.org/10.35377/saucis...1665478

AMA

1.Sahu P, Kumar A, Singh MK, Jain R, Upreti K, Parashar J. LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection. SAUCIS. 2025;8(2):184-197. doi:10.35377/saucis.1665478

Chicago

Sahu, Premananda, Ashwani Kumar, Mahesh K. Singh, Rituraj Jain, Kamal Upreti, and Jyoti Parashar. 2025. “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”. Sakarya University Journal of Computer and Information Sciences 8 (2): 184-97. https://doi.org/10.35377/saucis. 1665478.

EndNote

Sahu P, Kumar A, Singh MK, Jain R, Upreti K, Parashar J (June 1, 2025) LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection. Sakarya University Journal of Computer and Information Sciences 8 2 184–197.

IEEE

[1]P. Sahu, A. Kumar, M. K. Singh, R. Jain, K. Upreti, and J. Parashar, “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”, SAUCIS, vol. 8, no. 2, pp. 184–197, June 2025, doi: 10.35377/saucis...1665478.

ISNAD

Sahu, Premananda - Kumar, Ashwani - Singh, Mahesh K. - Jain, Rituraj - Upreti, Kamal - Parashar, Jyoti. “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 184-197. https://doi.org/10.35377/saucis. 1665478.

JAMA

1.Sahu P, Kumar A, Singh MK, Jain R, Upreti K, Parashar J. LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection. SAUCIS. 2025;8:184–197.

MLA

Sahu, Premananda, et al. “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 184-97, doi:10.35377/saucis. 1665478.

Vancouver

1.Premananda Sahu, Ashwani Kumar, Mahesh K. Singh, Rituraj Jain, Kamal Upreti, Jyoti Parashar. LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection. SAUCIS. 2025 Jun. 1;8(2):184-97. doi:10.35377/saucis. 1665478