A Comparison of Transfer Learning Techniques in Lung Cancer Nodule Detection
DOI:
https://doi.org/10.32628/CSEIT25112820Keywords:
Lung Cancer, resnet50, squeezenet, comparative analysis, Deep Learning(DL)Abstract
Lung Cancer continues to be a major global health hazard, contributing to around 1.8 million fatalities per year. This number is anticipated to increase, with projections forecasting 17 million deaths by 2030. Major risk factors include asbestos, tobacco smoke, air pollution, radon exposure, previous radiation treatment, and the family history of the disease. The recent Covid-19 pandemic has worsened the situation for lung cancer patients, making them more vulnerable to complications. In this study, two Convolutional Neural Network (CNN) models, SqueezeNet and Resnet-50, were evaluated for lung nodule classification in Computed Tomography (CT) images. These CNN models differ in architecture, depth, and feature extraction abilities. Key performance indicators such as classification sensitivity, specificity, accuracy, and computational efficiency are the main focus of the comparison. The Luna 16 database, which comprise CT images and labeled lung nodules, was used for model training and validation. The results showed that SqueezeNet outperformed ResNet-50, achieving a Train-accuracy of 88.07% and a Test-accuracy of 89.62%, while ResNet-50 achieved a Train-accuracy of 83.81% and a Test-accuracy of 86.18%. Both models demonstrated strong performance in evaluation metrics like F1-score, Precision and Recall, highlighting the effectiveness of CNN-based models in enhancing lung nodule detection.
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Chaturvedi, Pragya, Anuj Jhamb, Meet Vanani, and Varsha Nemade. "Prediction and classification of lung cancer using machine learning techniques." In IOP conference series: materials science and engineering, vol. 1099, no. 1, p. 012059. IOP Publishing, 2021.
Crasta, Lavina Jean, Rupal Neema, and Alwyn Roshan Pais. "A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis." Healthcare Analytics 5 (2024): 100316.
Guo, Chengquan, Yan Chen, and Jianjun Li. "Radiographic imaging and diagnosis of spinal bone tumors: AlexNet and ResNet for the classification of tumor malignancy." Journal of Bone Oncology (2024): 100629.
J. Alam, S. Alam, and A. Hossan, “Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier.”
Kanakaraddi, S. G., Handur, V. S., Jalannavar, A., Chikaraddi, A., & Giraddi, S. (2024). Segmentation and Classification of Lung Cancer using Deep Learning Techniques. Procedia Computer Science, 235, 3226-3235.
Kanavati, Fahdi, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, and Masayuki Tsuneki. "Weakly-supervised learning for lung carcinoma classification using deep learning." Scientific reports 10, no. 1 (2020): 9297.
Kansal, Kajal, Tej Bahadur Chandra, and Akansha Singh. "ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning" Session id: ICMLDsE. 004"." Procedia Computer Science 235 (2024): 70-80.
M. Vas and A. Dessai, “Lung cancer detection system using lung CT image processing,” 2017 Int.Conf. Comput. Commun. Control Autom. ICCUBEA 2017, pp. 1–5, 2018, doi:10.1109/ICCUBEA.2017.8463851. “(24) Severity of COVID-19 in patients with lung cancer”.
M. Yang et al., “Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images a narrative review with contemporary clinical recommendations,”
Makhdoomi, Shaif Mehraj, Cherry Khosla, and Sagar Dhanraj Pande. "Real Time Lung Cancer Classification with YOLOv5." EAI Endorsed Transactions on Pervasive Health and Technology 9 (2023)
Mohamed, Tehnan IA, Olaide N. Oyelade, and Absalom E. Ezugwu. "Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm." Plos one 18, no. 8 (2023): e0285796.
Mohite, A., 2021. Application of transfer learning technique for detection and classification of lung cancer using CT images. Int J Sci Res Manag, 9(11), pp.621-34.
N. Chaudhari and D. A. V Malviya, “IMAGE PROCESSING FOR DETECTION OF LUNG CANCER: A REVIEW,” 2021. [Online]. Available: www.ijcrt.org
Nazir, Imran, Ihsan ul Haq, Salman A. AlQahtani, Muhammad Mohsin Jadoon, and Mostafa Dahshan. "Machine Learning‐Based Lung Cancer Detection Using Multiview Image Registration and Fusion." Journal of Sensors 2023, no. 1 (2023): 6683438.
Neshat, Mehdi, Muktar Ahmed, Hossein Askari, Menasha Thilakaratne, and Seyedali Mirjalili. "Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs." Procedia Computer Science 235 (2024): 1841-1850.
P. Sadhukhan, M. T. Ugurlu, and M. O. Hoque, “Effect of covid-19 on lungs: Focusing on prospective malignant phenotypes,” Cancers, vol. 12, no. 12. MDPI AG, pp. 1–17, Dec. 01, 2020. doi: 10.3390/cancers12123822.
Pandian, R., V. Vedanarayanan, DNS Ravi Kumar, and R. Rajakumar. "Detection and classification of lung cancer using CNN and Google net." Measurement: Sensors 24 (2022): 100588.
Q. A. Al-Haija and A. Adebanjo, “Breast cancer diagnosis in histopathological images using ResNet50convolutionalneuralnetwork,”Sep.2020.doi:.10.1109/IEMTRONICS51293.2020.9216455
Rehman, M. Kashif, I. Abunadi, and N. Ayesha, “Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques,” in 2021 1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021, Apr. 2021, pp. 101–104. doi: 10.1109/CAIDA51941.2021.9425269.
S. Mukherjee and S. Bohra, “Issue 1 www.jetir.org (ISSN-2349-5162),” 2020. [Online]. Available: www.jetir.org SCAD College of Engineering and Technology and Institute of Electrical and Electronics Engineers, Proceedings of the International Conference on Trends in Electronics and Informatics (ICOEI 2019) :23-25, April 2019.
S. Walvekar and S. Shinde, “International Conference on Communication and Information Processing Detection of COVID-19 from CT Images Using resnet50,” 2020. [Online]. Available: https://ssrn.com/abstract=3648863
Šarić M, Russo M, Stella M and Sikora M 2019 CNN-based method for lung cancer detection in whole slide histopathology images Int. Conf. on Smart and Sustainable Technologies (SpliTech) pp. 1-4. .
Sori, W.J., Feng, J., Godana, A.W., Liu, S. and Gelmecha, D.J., 2021. DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Frontiers of Computer Science, 15, pp.1-13.
Suiçmez, Alihan, Çağrı Suiçmez, and Cengiz Tepe. "Lung Cancer Detection By Hybrid Learning Method Applying Smote Technique." Gazi University Journal of Science Part C: Design and Technology 10.4 (2022): 1098-1110.
Tekade, Ruchita, and K. Rajeswari. "Lung cancer detection and classification using deep learning." In 2018 4th international conference on computing communication control and automation (ICCUBEA), pp. 1-5. IEEE, 2018.
Tsivgoulis, Michail, Thomas Papastergiou, and Vasilis Megalooikonomou. "An improved SqueezeNet model for the diagnosis of lung cancer in CT scans." Machine Learning with Applications 10 (2022): 100399.
V. J. Pawar, “Lung Cancer Detection System Using Image Processing and Machine Learning Techniques,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5956–5963, Aug. 2020, doi: 10.30534/ijatcse/2020/260942020.
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