Modified Grey Wolf Optimization Algorithm (mGWO) For Detection and Diagnosis of Pancreatic Tumor Using Region Based Segmentation Techniques
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Abstract
Pancreatic cancers are strange progression of cell in intestinal enzymes and hormone generating cell. Pancreatic adenocarcinoma is perhaps the most severe threatening tumors which stay the fourth driving reason for the disease related demise. The anticipation for patients determined to have this pancreatic tumor has consistently remained remarkably poor. Unlike brain, pancreas is not secured by skull. It is bounded by numerous organs and greasy tissues in the stomach and hence recognition and dissection is highly inappropriate. Still, pancreatic cancer can be healed if it is noticed at a primary phase. But, most of the abdominal CT images contain noises in addition to the visceral fat in the proximity of pancreas makes it very challenging for timely detection. Various methodologies like Duck Traveller Optimization (DTO), Improved Duck Traveller Optimization (IDTO) and Grey Wolf Optimization (GWO) is utilized in this pancreatic cancer detection. The results are not much accurate by considering the above said methods. In this paper, an effort is made to identify pancreatic tumor using modified Grey Wolf Optimization (mGWO). Proper image processing procedures and a classifier is utilized to identify the tumor. After the pre-processing stage, smallest distance classifier is adopted towards noticing the tumorous part in the image. It is witnessed that the accurateness of discovering tumor is about97% in the proposed mGWO.
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