Early Diagnosis of Chronic and Acute Pancreatitis using Modern Soft Computing Techniques
R. Balakrishna1, R. Anandan2

1R. Balakrishna, Department of CSE, School of Engineering, Vels Institute of Science Technology and Advanced Studies VISTAS, Chennai, (Tamil Nadu), India.
2R. Anandan, Department of CSE, School of Engineering, Vels Institute of Science Technology and Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 25 December 2018 | Manuscript Published on 24 January 2019 | PP: 65-69 | Volume-7 Issue-4S2 December 2018 | Retrieval Number: Es2039017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The analysis of pancreatic cancer at an incipient stage is very crucial for raising the endurance of the patients. The infection of pancreas is called as Pancreatitis, either be acute (sudden and severe) or chronic (ongoing). Across the globe, Pancreatic cancer is the fourth most cause of cancer related to death and the most challenging aspect of pancreatic cancer is diagnosing at an incipient stage. The pancreas is a gland that disguises digestive enzymes as well as important hormones and the most common causes of chronic pancreatitis is heavy consumption of alcohol, followed by gallstones. Current work presents the creation of datasets which comprises of pancreas images and it is segregated by using Big Data analytics tools like Hadoop, Next the segregated images are preprocessed for removal of noises or any other disturbance which occurs in the images. The preprocessing is done by Wiener’s filter and the PSNR, MSE and SNR values are noted. Next the preprocessed image is segmented, In order to find the region of interest and the segmentation is performed by machine learning algorithm called Support Vector Machine (SVM).Finally we need to extract the features of the images in the region of interest identified in the segmentation process. The promising results indicate that pancreatic cancer can be diagnosed with high accuracy.
Keywords: Pancreas, Preprocessing, Pancreatitis, Segmentation, Extraction, Prediction and Incipient.
Scope of the Article: Soft Computing