Research Article
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Year 2025, Volume: 8 Issue: 2, 184 - 197

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

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

Abstract

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.

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

Year 2025, Volume: 8 Issue: 2, 184 - 197

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

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

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

Lung Cancer Deep Learning Machine Learning Low Dose CT Artificial Intelligence

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.
There are 42 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Premananda Sahu 0000-0002-9360-8423

Ashwani Kumar 0000-0002-2100-900X

Mahesh K. Singh 0009-0006-5036-6037

Rituraj Jain 0000-0002-5532-1245

Kamal Upreti 0000-0003-0665-530X

Jyoti Parashar 0000-0002-6573-4270

Early Pub Date June 13, 2025
Publication Date
Submission Date March 25, 2025
Acceptance Date April 21, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Sahu, P., Kumar, A., Singh, M. K., Jain, R., et al. (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 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. June 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. “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 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 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, 2025, doi: 10.35377/saucis...1665478.
ISNAD Sahu, Premananda et al. “LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 184-197. https://doi.org/10.35377/saucis. 1665478.
JAMA 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, 2025, pp. 184-97, doi:10.35377/saucis. 1665478.
Vancouver 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):184-97.
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