NDLI: Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

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Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

Content Provider	Tech Science Press
Author	Franklin, S. Wilfred Thanya, T. Franklin, S.
Abstract	At an early point, the diagnosis of pancreatic cancer is mediocre, since the radiologist is skill deficient. Serious threats have been posed due to the above reasons, hence became mandatory for the need of skilled technicians. However, it also became a time-consuming process. Hence the need for automated diagnosis became mandatory. In order to identify the tumor accurately, this research proposes a novel Convolution Neural Network (CNN) based superior image classification technique. The proposed deep learning classification strategy has a precision of 97.7%, allowing for more effective usage of the automatically executed feature extraction technique to diagnose cancer cells. Comparative analysis with CNN-Grey Wolf Optimization (GWO) is carried based on varied testing and training outcomes. The suggested study is carried out at a rate of 90%–10%, 80%–20%, and 70%–30%, indicating the robustness of the proposed research work. Outcomes show that the suggested method is effective. GWO-CNN is reliable and accurate relative to other detection methods available in the literatures.
Related Links	https://www.techscience.com/iasc/v36n1/49967
Starting Page	97
Ending Page	112
ISSN	10798587
DOI	10.32604/iasc.2023.029675
Issue Number	1
Journal	Intelligent Automation & Soft Computing (IASC)
Volume Number	36
e-ISSN	2326005X
Language	English
Publisher Date	2022-09-29
Access Restriction	Open
Subject Keyword	feature extraction grey wolf optimizer Convolution neural network deep learning technique
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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