Biomarker detection on Pancreatic cancer dataset using entropy based spectral clustering

Pahari, Purbanka; Basak, Piyali; Sarkar, Anasua

doi:10.1109/icrcicn.2017.8234508

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The optimization of pancreatic classification parameters has accuracy of 82.64% [6]. (Pahari et al) [7], suggested a segmentation approach for pancreatic tumor. In 2015, (Reddy et al) [8] developed the wavelet transformation technique with improvised classification and segmentation accuracy of 75.2% and 79.2% respectively.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

Thanya¹,

Franklin²

2023

Intelligent Automation &Amp; Soft Computing

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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.

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Section: Introductionmentioning

confidence: 99%