Enhancing Lung Cancer Diagnosis through CT Scan Image Analysis using Mask-EffNet
Keywords:
Auto encoder, CT scan image, lung cancer, EfficientNet, CNNAbstract
CT scans efficiently detect lung cancer. A good prediction method is crucial. Recently, deep convolutional neural networks (CNN) have influenced picture categorization algorithms. This article presents a hybrid strategy using an upgraded deep transfer learning EfficientNet and a masked autoencoder for image-based distribution estimation (MADE). MADE improves feature acquisition, dimensionality, uncertainty, imbalanced data, transfer learning, and model interpretability before lung cancer categorization. These benefits improve classification accuracy and data use. Mask-EffNet, the proposed model, has two phases. The initial phase uses MADE to extract features. Using a pre-trained EfficientNet model, types are classified next. Mask-EffNet is tested using EfficientNetB7. The study uses the "IQ-OTH/NCCD" benchmark dataset, which includes lung cancer patients classified as benign, malignant, or normal. Mask-EffNet has 98.98% test set accuracy with ROC scores of 0.9782–0.9872. We tested the suggested pre-trained Mask-EffNet against different CNN architectures. The EfficientNetB7-based Mask-EffNet outperforms various CNNs in accuracy and efficacy, as expected.
References
U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, "Cancer treatment," 2023. [Online]. Available: http://www.cancer.gov/about-cancer/treatment.
World Health Organization, "Cancer," 2023. [Online]. Available: http://www.who.int/health-topics/cancer#tab=tab_1.
M. S. Al-Huseiny and A. S. Sajit, "Transfer learning with GoogLeNet for detection of lung cancer," Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, pp. 1078–1086, 2021.
Anon, "Chest CT-Scan images Dataset," 2023. [Online]. Available: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images. Accessed: June 7, 2023.
Anon, "The IQ-OTH/NCCD lung cancer dataset — Kaggle," 2023. [Online]. Available: https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset. Accessed: Jan. 19, 2024.
D. B. Krishnamoorthy and S. Padhy, "AI Based Machine Learning Prediction Measure Parkinson Disease Severity," in International Conference on Machine Intelligence, Tools, and Applications, Cham: Springer Nature Switzerland, Apr. 2024, pp. 209–219.
D. Narin and T. Ö. Onur, "The effect of hyper parameters on the classification of lung cancer images using deep learning methods," Erzincan University Journal of Science and Technology, vol. 15, pp. 258–268, 2022.
N. E. Protonotarios, I. Katsamenis, S. Sykiotis, N. Dikaios, G. A. Kastis, S. N. Chatziioannou, et al., "A few-shot U-net deep learning model for lung cancer lesion segmentation via PET/CT imaging," Biomedical Physics & Engineering Express, vol. 8, no. 025019, 2022.
J. Chen, Q. Ma, and W. Wang, "A Lung Cancer Detection System Based on Convolutional Neural Networks and Natural Language Processing," in 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), 2021, pp. 354–359.
H. F. Kareem, M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, "Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset," Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, p. 1731, 2021.
S. Panigrahy, S. Dash, and S. Padhy, "Predictive Modelling of Diabetes Complications: Insights from Binary Classifier on Chronic Diabetic Mellitus," in 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), May 2024, pp. 1912–1920.
H. Alyasriy and A. Muayed, "The IQ-OTHNCCD lung cancer dataset," Mendeley Data, vol. 1, p. 1, 2020. Accessed: Jan. 19, 2023.
N. Kumar, Y. Dash, S. Padhy, S. Dash, and P. Suman, "Improving Breast Cancer Diagnosis: Insights From Machine Learning Models," in 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), May 2024, pp. 1668–1672.
R. Raza, F. Zulfiqar, M. O. Khan, M. Arif, A. Alvi, M. A. Iftikhar, and T. Alam, "Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images," Engineering Applications of Artificial Intelligence, vol. 126, p. 106902, 2023.
A. K. Dubey, G. L. Chabert, A. Carriero, A. Pasche, P. S. Danna, S. Agarwal, et al., "Ensemble deep learning derived from transfer learning for classification of COVID-19 patients on hybrid deep-learning-based lung segmentation: a data augmentation and balancing framework," Diagnostics, vol. 13, no. 11, p. 1954, 2023.
O. A. Jassim, M. J. Abed, and Z. H. S. Saied, "Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction," Baghdad Science Journal, 2023.
R. Wu, C. Liang, Y. Li, X. Shi, J. Zhang, and H. Huang, "Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT," Expert Systems with Applications, vol. 215, p. 119339, 2023.
R. AlGhamdi, T. O. Asar, F. Y. Assiri, R. A. Mansouri, and M. Ragab, "Al-Biruni earth radius optimization with transfer learning based histopathological image analysis for lung and colon cancer detection," Cancers, vol. 15, no. 13, p. 3300, 2023.
A. Y. Saleh, C. K. Chin, and R. A. Rosdi, "Transfer Learning for Lung Nodules Classification with CNN and Random Forest," Pertanika Journal of Science & Technology, vol. 32, no. 1, 2024.
S. Mammeri, M. Amroune, M. Y. Haouam, I. Bendib, and A. Corrêa Silva, "Early detection and diagnosis of lung cancer using YOLO v7, and transfer learning," Multimedia Tools and Applications, pp. 1–16, 2023.
T. T. Al-Shouka and K. M. A. Alheeti, "A Transfer Learning for Intelligent Prediction of Lung Cancer Detection," in 2023 Al-Sadiq International Conference on Communication and Information Technology (AICCIT), Jul. 2023, pp. 54–59.
R. Sharma, S. Kumar, A. Shrivastava, and T. Bhatt, "Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification," in Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing, Dec. 2023, pp. 1–8.
C. L. Fu, Z. B. Yang, P. Li, K. F. Shan, M. K. Wu, J. P. Xu, ... and F. H. Zhao, "Discrimination of ground‐glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study," Cancer Medicine, vol. 12, no. 18, pp. 18460–18469, 2023.
Z. Ren, Y. Zhang, and S. Wang, "A hybrid framework for lung cancer classification," Electronics, vol. 11, no. 10, p. 1614, 2022.
F. C. Laqua, P. Woznicki, T. A. Bley, M. Schöneck, M. Rinneburger, M. Weisthoff, ... and B. Baeßler, "Transfer-learning deep radiomics and hand-crafted radiomics for classifying lymph nodes from contrast-enhanced computed tomography in lung cancer," Cancers, vol. 15, no. 10, p. 2850, 2023.
S. U. Atiya, N. V. K. Ramesh, and B. N. K. Reddy, "Classification of non-small cell lung cancers using deep convolutional neural networks," Multimedia Tools and Applications, vol. 83, no. 5, pp. 13261–13290, 2024.
M. Humayun, R. Sujatha, S. N. Almuayqil, and N. Z. Jhanjhi, "A transfer learning approach with a convolutional neural network for the classification of lung carcinoma," in Healthcare, vol. 10, no. 6, p. 1058, Jun. 2022.
K. T. Chui, B. B. Gupta, R. H. Jhaveri, H. R. Chi, V. Arya, A. Almomani, and A. Nauman, "Multiround transfer learning and modified generative adversarial network for lung cancer detection," International Journal of Intelligent Systems, pp. 1–14, 2023.
T. Saikia, R. Kumar, D. Kumar, and K. K. Singh, "An automatic lung nodule classification system based on hybrid transfer learning approach," SN Computer Science, vol. 3, no. 4, p. 272, 2022.
S. Nigudgi and C. Bhyri, "Lung cancer CT image classification using hybrid-SVM transfer learning approach," Soft Computing, vol. 27, no. 14, pp. 9845–9859, 2023.
S. Dadgar and M. Neshat, "Comparative hybrid deep convolutional learning framework with transfer learning for diagnosis of lung cancer," in International Conference on Soft Computing and Pattern Recognition, Cham: Springer Nature Switzerland, Dec. 2022, pp. 296–305.
F. Zhu, R. Zhong, F. Li, C. Li, N. Din, H. Sweidan, ... and Z. Pan, "Development and validation of a deep transfer learning-based multivariable survival model to predict overall survival in lung cancer," Translational Lung Cancer Research, vol. 12, no. 3, p. 471, 2023.
S. Wang, L. Dong, X. Wang, and X. Wang, "Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy," Open Medicine, vol. 15, no. 1, pp. 190–197, 2020.
R. Patnaik, P. S. Rath, S. Padhy, and S. Dash, "A Novel Optimized Colonic Adenocarcinoma Detection using Deep Transfer Learning Approach with XceptionTS Model," Journal of Electrical Systems, vol. 20, no. 7s, pp. 816–830, 2024.
A. B. Dash, S. Dash, and S. Padhy, "Identification of Foxtail Millet Solution for Malignant Colorectal Cell Growth Reduction Using Deep Learning-Based Mobile Net Model," in The Role of Women in Cultivating Sustainable Societies Through Millets, IGI Global, 2024, pp. 42–59.
S. Panigrahy, S. Dash, and S. Padhy, "Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization," International Journal of Advanced Computer Science & Applications, vol. 15, no. 3, 2024.
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