Machine Learning Based on Transfer Learning for Predicting Lung and Colon Cancer
DOI:
https://doi.org/10.22401/anjs.29.3.3129Keywords:
Machine learning, Deep learning, Transfer learning, Cancer diagnosis, Lung and colon cancerAbstract
Lung and colon (LC) cancer is the leading cause of death from cancer worldwide. Early detection is key in this disease, which at the same time proves to be a challenge because, in its early stages, symptoms are few. Present a method that offers a comprehensive approach using machine learning (ML) and deep learning (DL) for the early detection of LC cancer, achieved through analysis of histopathology images (HPI). Used modified transfer learning (TL) models, which we augmented with average-pooling 2D layers for better feature extraction from the HPI dataset. Then, feed these features into ML classifiers to evaluate their performance. Achieved very impressive results across the board, with the EfficientNet B2 and the SVC classifier reporting an accuracy, recall, F1 score, and precision of 0.9996, and an AUC of 0.9998, which outperforms present methods. Also, we put forth the fact that the use of DL and ML has great promise in health care, in particular in middle- and low-income countries, which have limited access to advanced diagnostic tools. Our proposed system may support early detection, which in turn may reduce mortality rates and improve health care outcomes.
References
[1] Siegel, R. L.; Miller, K. D.; Fuchs, H. E.; Jemal, A.; "Cancer statistics, 2022". CA. Cancer J. Clin., 72 (1): 7–33, 2022.
[2] Mareel, M.; Leroy, A.; "Clinical, Cellular, and Molecular Aspects of Cancer Invasion". Physiol. Rev., 83 (2): 337–376, 2003.
[3] Liu, P.; Lv, H.; Li, Y.; Liu, Z.; Yang, X.; Hu, H.; "Global bone cancer incidence and death rate analysis at 40 years". Discov. Oncol., 16 (1): 1087–1111, 2025.
[4] Kurishima, K.; Miyazaki, K.; Watanabe, H.; Shiozawa, T.; Ishikawa, H.; Satoh, H.; Hizawa, N.; "Lung cancer patients with synchronous colon cancer". Mol. Clin. Oncol., 8 (1): 137–140, 2017.
[5] Del Re, M.; Rofi, E.; Restante, G.; Crucitta, S.; Arrigoni, E.; Fogli, S.; Di Maio, M.; Petrini, I.; Danesi, R.; "Implications of KRAS mutations in acquired resistance to treatment in NSCLC". Oncotarget, 9 (5): 6630–6643, 2017.
[6] Baqir A. Altimmime; Farah A. Rashid; "View of CXCL12 as a Metastasis Inducer Chemokine of Breast Cancer". Al-Nahrain J. Sci., 27 (5): 49–57, 2024.
[7] A. Maryoosh, A.; Pashazadeh, S.; "Leukemia Detection using Machine Learning Algorithms: Current trends and future directions". Mustansiriyah J. Pure Appl. Sci., 2 (3): 33–49, 2024.
[8] van der Velden, B. H. M.; Kuijf, H. J.; Gilhuijs, K. G. A.; Viergever, M. A.; "Explainable artificial intelligence (XAI) in deep learning-based medical image analysis". Med. Image Anal., 79: 102470, 2022.
[9] Madabhushi, A.; Lee, G.; "Image analysis and machine learning in digital pathology: Challenges and opportunities". Med. Image Anal., 33: 170–175, 2016.
[10] Barragán-Montero, A.; Javaid, U.; Valdés, G.; Nguyen, D.; Desbordes, P.; Macq, B.; Willems, S.; Vandewinckele, L.; Holmström, M.; Löfman, F.; Michiels, S.; Souris, K.; Sterpin, E.; Lee, J. A.; "Artificial intelligence and machine learning for medical imaging: A technology review". Phys. Medica, 83: 242–256, 2021.
[11] Alaa Hussein; Mohammed Sahib Mahdi Altaei; "View of Deep Learning Techniques for Detecting and Segmenting Text in Natural Scene Images_ Review". Al-Nahrain J. Sci., 27 (2): 133–144, 2024.
[12] Masud, M.; Sikder, N.; Nahid, A.-A.; Bairagi, A. K.; AlZain, M. A.; "A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework". Sensors, 21 (3): 748, 2021.
[13] Talukder, M. A.; Islam, M. M.; Uddin, M. A.; Akhter, A.; Hasan, K. F.; Moni, M. A.; "Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning". Expert Syst. Appl., 205: 117695, 2022.
[14] Hage Chehade, A.; Abdallah, N.; Marion, J.-M.; Oueidat, M.; Chauvet, P.; "Lung and colon cancer classification using medical imaging: a feature engineering approach". Phys. Eng. Sci. Med., 45 (3): 729–746, 2022.
[15] Mehmood, S.; Ghazal, T. M.; Khan, M. A.; Zubair, M.; Naseem, M. T.; Faiz, T.; Ahmad, M.; "Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing". IEEE Access, 10: 25657–25668, 2022.
[16] Singh, O.; Singh, K. K.; "An approach to classify lung and colon cancer of histopathology images using deep feature extraction and an ensemble method". Int. J. Inf. Technol., 15 (8): 4149–4160, 2023.
[17] Mengash, H. A.; Alamgeer, M.; Maashi, M.; Othman, M.; Hamza, M. A.; Ibrahim, S. S.; Zamani, A. S.; Yaseen, I.; "Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis". Cancers (Basel)., 15 (5): 1591, 2023.
[18] Prusty, S.; Patnaik, S.; Dash, S. K.; "EfficientNetB7: To predict lung and colon cancer at early stages". in RECENT TRENDS IN APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING, 2819(1) 030013, 2023.
[19] Reddy, N.; Parasa, G.; Janani, R.; Thiruveedula, S.; Sujatha, K.; Varaprasad Rao, B.; N, S. M.; "Modernizing Cancer Diagnosis with an Intelligent Model for Lung and Colon Cancer". J. Electr. Syst., 20 (6): 1218–1226, 2024.
[20] Aitazaz, T.; Tubaishat, A.; Al-Obeidat, F.; Shah, B.; Zia, T.; Tariq, A.; "Transfer learning for histopathology images: an empirical study". Neural Comput. Appl., 35 (11): 7963–7974, 2023.
[21] Niu, S.; Liu, Y.; Wang, J.; Song, H.; "A Decade Survey of Transfer Learning (2010–2020)". IEEE Trans. Artif. Intell., 1 (2): 151–166, 2020.
[22] Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H.; Xiong, H.; He, Q.; "A Comprehensive Survey on Transfer Learning". Proc. IEEE, 109 (1): 43–76, 2021.
[23] Marques, G.; Agarwal, D.; de la Torre Díez, I.; "Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network". Appl. Soft Comput., 96: 106691–106702, 2020.
[24] Petrini, D. G. P.; Shimizu, C.; Roela, R. A.; Valente, G. V.; Folgueira, M. A. A. K.; Kim, H. Y.; "Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network". IEEE Access, 10: 77723–77731, 2022.
[25] Srinivasu, P. N.; SivaSai, J. G.; Ijaz, M. F.; Bhoi, A. K.; Kim, W.; Kang, J. J.; "Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM". Sensors, 21 (8): 2852–2879, 2021.
[26] Wang, W.; Li, Y.; Zou, T.; Wang, X.; You, J.; Luo, Y.; "A Novel Image Classification Approach via Dense-MobileNet Models". Mob. Inf. Syst., 2020 (1): 1–8, 2020.
[27] Hsu, C.-C.; Jian, C.-Y.; Tu, E.-S.; Lee, C.-M.; Chen, G.-L.; "Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat". IEEE Trans. Geosci. Remote Sens., 62: 1–16, 2024.
[28] Singh, R.; Rana, R.; Singh, S. K.; "Performance Evaluation of VGG models in Detection of Wheat Rust". Asian J. Comput. Sci. Technol., 7 (3): 76–81, 2018.
[29] Tabrizchi, H.; Parvizpour, S.; Razmara, J.; "An Improved VGG Model for Skin Cancer Detection". Neural Process. Lett., 55 (4): 3715–3732, 2023.
[30] Zhao, C.; Shuai, R.; Ma, L.; Liu, W.; Hu, D.; Wu, M.; "Dermoscopy Image Classification Based on StyleGAN and DenseNet201". IEEE Access, 9: 8659–8679, 2021.
[31] Zhang, J.; Lu, C.; Li, X.; Kim, H.-J.; Wang, J.; "A full convolutional network based on DenseNet for remote sensing scene classification". Math. Biosci. Eng., 16 (5): 3345–3367, 2019.
[32] Breiman, L.; "Random Forests". Mach. Learn., 45 (1): 5–32, 2001.
[33] Cover, T.; Hart, P.; "Nearest neighbor pattern classification". IEEE Trans. Inf. Theory, 13 (1): 21–27, 1967.
[34] Zhang, S.; "Challenges in KNN Classification". IEEE Trans. Knowl. Data Eng., 34 (10): 4663–4675, 2022.
[35] Hastie, T.; Tibshirani, R.; Friedman, J.; ‘The Elements of Statistical Learning Data Mining, Inference, and Prediction’. 2nd ed.; Springer New York:, New York, NY, USA, 2009.
[36] Liao, H.; Zhang, X.; Zhao, C.; Chen, Y.; Zeng, X.; Li, H.; "LightGBM: an efficient and accurate method for predicting pregnancy diseases". J. Obstet. Gynaecol. (Lahore)., 42 (4): 620–629, 2022.
[37] Zhang, Z.; Wang, S.; Lin, N.; Wang, Z.; Liu, P.; "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM". Sustainability, 15 (3): 2052, 2023.
[38] Hawwal jasim, A.; Haider k. Hoomod; "Integrative Analysis of Facial and Bodily Expressions for Enhanced Emotion Recognition Using SVM and CNN in Python". Mustansiriyah J. Pure Appl. Sci., 3 (2): 1–11, 2025.
[39] Platt, J. C.; "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods". Adv. large margin Classif. 10.3, 61–74, 1999.
[40] Goh, K.-S.; Chang, E. Y.; Li, B.; "Using one-class and two-class SVMs for multiclass image annotation". IEEE Trans. Knowl. Data Eng., 17 (10): 1333–1346, 2005.
[41] Gong, M.; "A Novel Performance Measure for Machine Learning Classification". Int. J. Manag. Inf. Technol., 13 (1): 11–19, 2021.
[42] Baldi, P.; "Deep Learning in Biomedical Data Science". Annu. Rev. Biomed. Data Sci. Annu. Rev. Biomed. Data Sci. 2018, 8: 181–205, 2024.
[43] Fawcett, T.; "ROC Graphs: Notes and Practical Considerations for Researchers". Mach. Learn. 31.1, 1–38, 2004.
[44] Vergara, I. A.; Norambuena, T.; Ferrada, E.; Slater, A. W.; Melo, F.; "StAR: a simple tool for the statistical comparison of ROC curves". BMC Bioinformatics, 9 (1): 265, 2008.
[45] Fawcett, T.; "An introduction to ROC analysis". Pattern Recognit. Lett., 27 (8): 861–874, 2006.
[46] Yulianto, A.; Sukarno, P.; Suwastika, N. A.; "Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset". J. Phys. Conf. Ser., 1192: 012018, 2019.
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Copyright (c) 2026 Mohammed M. Neamah, Laith A. Al-Ani, Loay E. George
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