Pancreatic Cancer Detection Using Quaternion Wavelet Transform and Squeeze-and-Excitation Network with SVM Classifier

Abstract
Pancreatic cancer (PC) remains one of the most lethal malignancies worldwide, primarily due to the difficulty of early diagnosis and the subtle radiological signatures it presents. To address this challenge, we propose a hybrid computer-aided diagnostic framework that integrates the Quaternion Wavelet Transform (QWT) for robust multi-scale and phase-preserving feature extraction, a Squeeze-and-Excitation (SE) network for adaptive channel-wise feature recalibration, and a Support Vector Machine (SVM) classifier for reliable categorization of pancreatic lesions. The QWT effectively captures discriminative structural information, while the SE network enhances representational quality by modeling inter-channel dependencies. The fused features are subsequently classified by the SVM to ensure efficient and accurate decision-making. Experiments conducted on the publicly available Kaggle CT dataset demonstrate that the proposed method achieves an accuracy of 96.40%, a precision of 95.50%, a recall of 97.05%, a specificity of 96.72%, and an F1-score of 95.73%, outperforming several state-of-the-art approaches. These results highlight the potential of combining QWT, SE networks, and SVM in advancing computer-aided diagnosis for PC and suggest a promising direction for clinical translation.
Keywords
Pancreatic Cancer, Quaternion Wavelet Transform, Squeeze-and-Excitation Network, Support vector machine
References
- Sung H, Ferlay J, Siegel RL, etal. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality world-wide for 36 cancers in 185 countries. CA Cancer JC lin 2021; 71:209–49.
- Surveillance, Epidemiology, and End Results Program, Cancer stat facts— pancreas cancer, 2023, National Cancer Institute. https://seer.cancer.gov/statfacts/html/pancreas.html. Accessed February 2, 2024.
- Han S H, Heo J S, Choi S H, etal. Actual long-termout-come of T1 and T2 pancreatic ductal adenocarcinoma after surgical resection. Int J Surg 2017; 40:68–72.
- Van Roessel S, Kasumova G G, Verheij J, etal. International validation of the Eighth Edition of the American Joint Committee on Cancer (AJCC) TNM staging system in patients with resected pancreatic cancer. JAMA Surg 2018;153: e183617.
- Klatte D C F, Boekestijn B, Wasser M N J M, etal. Pancreatic cancer surveillance in carriers of a germline CDKN2A pathogenic variant: yield and outcomes of a 20-year prospective follow-up. J Clin Oncol 2022; 40: 3267–77.
- Tripathi, S. Artificial Intelligence: A Brief Review. In Analyzing Future Applications of AI, Sensors, and Robotics in Society; IGI Global: Hershey, PA, USA, 2021.
- Si K, Xue Y, Yu X, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors. Theranostics 2021; 11:1982.
- Liu KL, Wu T, Chen PT, et al. Deep learning to distinguish pancreatic cancer tissue from noncancerous pancreatic tissue: a retrospective study with crossracial external validation. Lancet Digital Health 2020; 2:e303–e313.
- Zhu Z, Xia Y, Xie L, et al. Multiscale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22. Springer International Publishing; 2019:3–12.
- Chu LC, Park S, Kawamoto S, et al. Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J Am Coll Radiol 2019; 16:1338–1342.
- Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging 2019; 38:1777–1787.
- Zhou Y, Li Y, Zhang Z, et al. Hyper-pairing network for multiphase pancreatic ductal adenocarcinoma segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22. Springer International Publishing; 2019:155–163.
- Alves N, Schuurmans M, Litjens G, et al. Fully automatic deep learning framework for pancreatic ductal adenocarcinoma detection on computed tomography. Cancers 2022; 14:376.
- Hameed BS, Krishnan UM. Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers 2022; 14:5382.
- Mahmoudi T, Kouzahkanan ZM, Radmard AR, et al. Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors. Sci Rep 2022; 12:3092.
- Tonozuka R, Itoi T, Nagata N, et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study. J Hepatobiliary Pancreat Sci 2021; 28:95–104.
- Zhang Z, Li S, Wang Z, Lu Y. A novel and efficient tumor detection framework for pancreatic cancer via CT images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2020:1160–1164.
- Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., & Summers, R. M. (2016). Data From Pancreas-CT (Version 2) [Data set]. The Cancer Imaging Archive.
- Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 9992-10002, doi: 10.1109/ICCV48922.2021.00986.
- J. Hu, L. Shen and G. Sun, "Squeeze-and-Excitation Networks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 7132-7141, doi: 10.1109/CVPR.2018.00745.
- C. Sekhar, K. Pavani and M. S. Rao, "Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey," 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India, 2021, pp. 1-5, doi: 10.1109/ICCICA52458.2021.9697291.
- Gao, X.; Wang, X. Performance of Deep Learning for Differentiating Pancreatic Diseases on Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study. Diagn. Interv. Imaging 2020, 101, 91–100.
- Deng, Y.; Ming, B.; Zhou, T.; Wu, J.-L.; Chen, Y.; Liu, P.; Zhang, J.; Zhang, S.-Y.; Chen, T.-W.; Zhang, X.-M. Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions. Front. Oncol. 2021, 11, 620981.
- Qureshi, T.A.; Gaddam, S.; Wachsman, A.M.; Wang, L.; Azab, L.; Asadpour, V.; Chen, W.; Xie, Y.; Wu, B.; Pandol, S.J.; et al. Predicting Pancreatic Ductal Adenocarcinoma Using Artificial Intelligence Analysis of Pre-Diagnostic Computed Tomography Images. Cancer Biomark. 2022, 33, 211–217.
- Mohamad Sehmi, M.N., Ahmad Fauzi, M.F., Wan Ahmad, W.S.H.M. and Wan Ling Chan, E., 2022. Pancreatic cancer grading in pathological images using deep learning convolutional neural networks. F1000Research, 10, p.1057.
- Upendra, V. and Puviarasi, R., 2022. Pancreatic Cancer Prediction Using Hierarchical Convolutional Neural Network and Visual Geometry Group16 CNN Approach on Accuracy and Specificity Performance Measures. ECS Transactions, 107(1), p.11927.
- Khasawneh, H., Patra, A., Rajamohan, N., Suman, G., Klug, J., Majumder, S., Chari, S.T., Korfiatis, P. and Goenka, A.H., 2022. Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists. Journal of Computer Assisted Tomography, pp.10-1097.
- Chen, X., Wang, W., Jiang, Y. and Qian, X., 2023. A dual-transformation with contrastive learning framework for lymph node metastasis prediction in pancreatic cancer. Medical Image Analysis, 85, p.102753.
- Chen, P.T., Wu, T., Wang, P., Chang, D., Liu, K.L., Wu, M.S., Roth, H.R., Lee, P.C., Liao, W.C. and Wang, W., 2023. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology, 306(1), pp.172-182.
- Chegireddy, R.P.R. and Srinagesh, A., 2023. A Novel Method for Human MRI Based Pancreatic Cancer Prediction Using Integration of Harris Hawks Varients & VGG16: A Deep Learning Approach. Informatica, 47(1).
- Sarac D, Badza Atanasijevic M, Mitrovic Jovanovic M, Kovac J, Lazic L, Jankovic A, Saponjski DJ, Milosevic S, Stosic K, Masulovic D, Radenkovic D, Papic V, Djuric-Stefanovic A. Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning. Cancers (Basel). 2025 Mar 27;17(7):1119. doi: 10.3390/cancers17071119. PMID: 40227615; PMCID: PMC11987955.
- A. Pravamanjari, S. Swain and P. K. Mallick, "Advanced Detection and Classification of Pancreatic Cancer in CT Images Using Swin Transformer Architecture," 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC), Bhubaneswar, India, 2025, pp. 288-293, doi: 10.1109/ESIC64052.2025.10962786.
- Viriyasaranon T, Chun JW, Koh YH, Cho JH, Jung MK, Kim SH, Kim HJ, Lee WJ, Choi JH, Woo SM. Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study. Cancers (Basel). 2023 Jun 28;15(13):3392. doi: 10.3390/cancers15133392. PMID: 37444502; PMCID: PMC10340780.