Deep Learning Algorithms to Detect Human Pancreatic Cancer from MRI Scan Images
Keywords:
Detection, Classification, Pancreatic Cancer, Magnetic resonance imaging, Computer Aided Detection, Deep learningAbstract
The idea of this project is to implement a Computer-Aided Detection system (CAD) for the early detection of pancreatic tumors based on the UNet++ architecture. Contrast Limited Adaptive Histogram Equalization (CLAHE) and Boosted Anisotropic Diffusion Filter (BADF) methods are used to enhance the MRI image. The pancreatic region associated with a lesion is precisely separated from the MRI image by segmentation. The best subset of texture characteristics is assessed by creating a classification system based on texture features that integrate HHO-based CNN and HHO-based Bag of visual terms. This will enhance classification accuracy. Transfer learning and Fine-tuning model using VGG 16 classifiers are utilised to create an automated system for classifying different grades of tumors in MRI Images. For various tumor classes, quantitative analysis is performed. The accuracy of the classification of the proposed classifier is validated and it is compared with the state-of-the-art approach.
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Alexakis, E.B. and Armenakis, C., 2020. Evaluation of UNet and UNet++ Architectures in High-Resolution Image Change Detection Applications. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, pp.15071514.
Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction.
Yang, J., Xu, R., Wang, C., Qiu, J., Ren, B. and You, L., 2021. Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review. Cancer Communications.
Alabool, H.M., Alarabiat, D., Abualigah, L. and Heidari, A.A., 2021. Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Computing and Applications, pp.142.
Arbane, M., Benlamri, R., Brik, Y. and Djerioui, M., 2021, February. Transfer Learning for Automatic Brain Tumor Classification Using MRI Images. In 2020 2nd International Workshop on HumanCentric Smart Environments for Health and Wellbeing (IHSH) (pp. 210214). IEEE.
Enriquez, J.S., Chu, Y., Pudakalakatti, S., Hsieh, K.L., Salmon, D., Dutta, P., Millward, N.Z., Lurie, E., Millward, S., McAllister, F. and Maitra, A., 2021. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Medical Informatics, 9(6), p.e26601.
Chen, X., Lin, X., Shen, Q. and Qian, X., 2020. Combined Spiral Transformation and ModelDriven MultiModal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer. IEEE Transactions on Medical Imaging, 40(2), pp.735747.
Jiang, S. and Li, Y., 2021. A comparative analysis of CT and MRI in differentiating pancreatic cancer from mass pancreatitis. American journal of translational research, 13(6), p.6431.
Nagpal G, Sharma M, Kumar S, Chaudhary K, Gupta S, Gautam A, Raghava GP. PCMdb: pancreatic cancer methylation database.
Bieliuniene E, Brøndum Frøkjær J, Pockevicius A, Kemesiene J, Lukosevičius S, Basevicius A, Atstupenaite V, Barauskas G, Ignatavicius P, Gulbinas A, Dambrauskas Z. CT- and MRI-based assessment of body composition and pancreatic fibrosis reveals a high incidence of clinically significant metabolic changes that affect the quality of life and treatment outcomes of patients with chronic pancreatitis and pancreatic cancer. Medicine (Kaunas) 2019; 55:649.
Dite P, Novotny I, Dvorackova J, Kianicka B, Blaho M, Svoboda P, Uvirova M, Rohan T, Maskova H, Kunovsky L. Pancreatic solid focal lesions: differential diagnosis between autoimmune pancreatitis and pancreatic cancer. Digestive diseases (Basel, Switzerland) 2019;37:1–6.
Almeida RR, Lo GC, Patino M, Bizzo B, Canellas R, Sahani DV. Advances in pancreatic CT imaging. AJR Am J Roentgenol. 2018;211:52–66.
Dalah E, Erickson B, Oshima K, Schott D, Hall WA, Knechtges P, Li XA.Correl Paulson E, Tai A, the notion of ADC with pathological treatment response for radiation therapy of pancreatic cancer. Transl Oncol. 2018;11:391–398.
Liu, K. L., Wu, T., Chen, P. T., Tsai, Y. M., Roth, H., Wu, M. S., & Wang, W. (2020). Deep learning to distinguish pancreatic cancer tissue from noncancerous pancreatic tissue: a retrospective study with crossracial external validation.
Liu, S. L., Li, S., Guo, Y. T., Zhou, Y. P., Zhang, Z. D., Li, S., & Lu, Y.(2019). Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster regionbased convolution neural network.
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