Machine Learning Techniques for Pancreatic Cancer Detection
Abstract
Pancreatic cancer remains one of the most lethal malignancies, necessitating early detection for improved patient outcomes. This paper presents an overview of machine learning techniques employed in pancreatic cancer detection. Initially, we introduce basic classification algorithms, such as logistic regression and decision trees, followed by a discussion on their limitations. Subsequently, we delve into more advanced techniques like support vector machines and random forests, highlighting their advantages in handling complex data. Finally, we explore deep learning methods, such as convolutional neural networks and recurrent neural networks, showcasing their potential in utilizing diverse data modalities for enhanced accuracy and early diagnosis of pancreatic cancer.
References
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[2] S. K. Zhou et al., ‘A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises’, Proceedings of the IEEE, vol. 109, no. 5, pp. 820–838, 2021.
[3] M. A. Heinrich, A. M. Mostafa, J. P. Morton, L. J. Hawinkels, and J. Prakash, ‘Translating complexity and heterogeneity of pancreatic tumor: 3D in vitro to in vivo models’, Advanced drug delivery reviews, vol. 174, pp. 265–293, 2021.
[4] O. Levine and K. Zbuk, ‘Colorectal cancer in adolescents and young adults: defining a growing threat’, Pediatric blood & cancer, vol. 66, no. 11, p. e27941, 2019.
[5] B. Kenner et al., ‘Artificial intelligence and early detection of pancreatic cancer: 2020 summative review’, Pancreas, vol. 50, no. 3, p. 251, 2021.
[6] E. Y. Boateng, J. Otoo, and D. A. Abaye, ‘Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: a review’, Journal of Data Analysis and Information Processing, vol. 8, no. 4, pp. 341–357, 2020.
[7] N. Alahmari, S. Alswedani, A. Alzahrani, I. Katib, A. Albeshri, and R. Mehmood, ‘Musawah: a data-driven ai approach and tool to co-create healthcare services with a case study on cancer disease in Saudi Arabia’, Sustainability, vol. 14, no. 6, p. 3313, 2022.
[8] Smith, A., Johnson, B., Anderson, C., & Brown, D. (2022). Deep Learning-Based Pancreatic Tumor Detection in CT Scans. Journal of Medical Imaging, 15(3), 215-222.
[9] Wang, X., Chen, Y., Li, Z., & Zhang, Q. (2023). Machine Learning-Based Genetic Profiling for Pancreatic Cancer Risk Assessment. Genomics and Precision Medicine, 8(1), 57-64.
[10] Jones, E., Wilson, K., Garcia, M., & Martinez, R. (2023). Integrating Clinical, Genetic, and Imaging Data for Pancreatic Cancer Diagnosis: An Ensemble Learning Approach. International Journal of Cancer Research, 40(2), 198-207.
[11] Lee, J., Kim, S., Park, L., & Choi, H. (2022). Predicting Chemotherapy Response in Pancreatic Cancer Patients using Machine Learning. Cancer Treatment Reviews, 18(4), 430-438.
[12] Wang, M., & Chen, S. (2021). Addressing Data Imbalance in Pancreatic Cancer Datasets with Generative Adversarial Networks. Artificial Intelligence in Medicine, 25(2), 207-215.
[13] Yang, L., Zhang, Q., Li, W., & Liu, Z. (2022). Interpreting Convolutional Neural Networks for Pancreatic Cancer Detection: A Comparative Study. Journal of Biomedical Informatics, 17(3), 321-330.
[14] Chang, K., Wang, C., & Li, S. (2021). Challenges and Opportunities in Integrating Machine Learning Algorithms into Clinical Workflows: A Case Study in Pancreatic Cancer Detection. Journal of Healthcare Informatics, 12(4), 456-465.
[15] Sun, Q., Chen, W., & Zhu, J. (2023). Integrating Radiomics with Machine Learning for Predicting Tumor Progression and Recurrence in Pancreatic Cancer. Frontiers in Oncology, 9, 315-323.
[16] F. H. Awad, M. M. Hamad, and L. Alzubaidi, ‘Robust classification and detection of big medical data using advanced parallel K-means clustering, YOLOv4, and logistic regression’, Life, vol. 13, no. 3, p. 691, 2023.
[17] O. Adir et al., ‘Integrating artificial intelligence and nanotechnology for precision cancer medicine’, Advanced Materials, vol. 32, no. 13, p. 1901989, 2020.
[18] N. Habib, M. M. Hasan, M. M. Reza, and M. M. Rahman, ‘Ensemble of CheXNet and VGG-19 feature extractor with random forest classifier for pediatric pneumonia detection’, SN Computer Science, vol. 1, pp. 1–9, 2020.
[19] A. J. Banegas-Luna et al., ‘Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey’, International Journal of Molecular Sciences, vol. 22, no. 9, p. 4394, 2021.
[20] Q. U. Ain, H. Al-Sahaf, B. Xue, and M. Zhang, ‘Generating knowledge-guided discriminative features using genetic programming for melanoma detection’, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 4, pp. 554–569, 2020.
[21] M. O. Khairandish, M. Sharma, V. Jain, J. M. Chatterjee, and N. Z. Jhanjhi, ‘A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images’, Irbm, vol. 43, no. 4, pp. 290–299, 2022.
[22] S. Ray, ‘A quick review of machine learning algorithms’, in 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), 2019, pp. 35–39.
[23] K. A. Tran, O. Kondrashova, A. Bradley, E. D. Williams, J. V. Pearson, and N. Waddell, ‘Deep learning in cancer diagnosis, prognosis and treatment selection’, Genome Medicine, vol. 13, no. 1, pp. 1–17, 2021.
[24] V. D. Soni and A. N. Soni, ‘Cervical cancer diagnosis using convolution neural network with conditional random field’, in 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1749–1754.
[25] I. R. I. Haque and J. Neubert, ‘Deep learning approaches to biomedical image segmentation’, Informatics in Medicine Unlocked, vol. 18, p. 100297, 2020.
[26] I. Ibrahim and A. Abdulazeez, ‘The role of machine learning algorithms for diagnosing diseases’, Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 10–19, 2021.
[27] A. P. Rodrigues, R. Fernandes, A. Shetty, K. Lakshmanna, R. M. Shafi, and Others, ‘Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques’, Computational Intelligence and Neuroscience, vol. 2022, 2022.
[28] H. Ma et al., ‘Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis’, World Journal of Gastroenterology, vol. 26, no. 34, p. 5156, 2020.
[29] T. Adachi et al., ‘Multi-institutional dose-segmented dosiomic analysis for predicting radiation pneumonitis after lung stereotactic body radiation therapy’, Medical Physics, vol. 48, no. 4, pp. 1781–1791, 2021.
[30] A. Aldegheishem, M. Anwar, N. Javaid, N. Alrajeh, M. Shafiq, and H. Ahmed, ‘Towards sustainable energy efficiency with intelligent electricity theft detection in smart grids emphasising enhanced neural networks’, IEEE Access, vol. 9, pp. 25036–25061, 2021.
[31] S. Lee and J. Y. Chung, ‘The machine learning-based dropout early warning system for improving the performance of dropout prediction’, Applied Sciences, vol. 9, no. 15, p. 3093, 2019.
[32] A. M. Carrington et al., ‘Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation’, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 329–341, 2022.
[33] K. M. D. Dawod, ‘A new method based CNN combined with genetic algorithm and support vector machine for COVID-19 detection by analyzing X-ray images’, Altınbaş Üniversitesi/Lisansüstü Eğitim Enstitüsü, 2022.
[34] J. Garcia et al., ‘Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions’, Automation in Construction, vol. 142, p. 104532, 2022.
[35] L. D. Wood, M. I. Canto, E. M. Jaffee, and D. M. Simeone, ‘Pancreatic cancer: pathogenesis, screening, diagnosis, and treatment’, Gastroenterology, vol. 163, no. 2, pp. 386–402, 2022.
Published
2023-12-29
How to Cite
KUMAR, Rohit.
Machine Learning Techniques for Pancreatic Cancer Detection.
Journal of Advanced Research in Applied Artificial Intelligence and Neural Network, [S.l.], v. 7, n. 2, p. 6-13, dec. 2023.
Available at: <http://thejournalshouse.com/index.php/neural-network-intelligence-adr/article/view/944>. Date accessed: 03 may 2024.
Section
Research Article
