DETECTING PANCREATIC CANCER WITH MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
Main Article Content
Abstract
The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Zuherman Rustam, FildzahZhafarina, Glori Stephani Saragih, Sri Hartini; Pancreatic cancer classification using
logistic regression and random forest.
Emmanuel Briones, Angelyn Lao, Geoffrey A. Solano; A Pancreatic Cancer Detection Support Tool Using Mass
Spectrometry Data and Support Vector Machines
Wazir Muhammad, Gregory R. Hart, Bradley Nartowt, James J. Farrell, Kimberly Johung, Ying Liang1 and Jun
Deng; Pancreatic Cancer Prediction through an Artificial Neural Network.
WismajiSadewo, Zuherman Rustam, Hamidah Hamidah and AlifahRoudhohChusmarsyah; Pancreatic Cancer
Early Detection Using Twin Support Vector Machine Based on Kernel.
Kao-Lang Liu, Tinghui Wu, Po-Ting Chen, Yuhsiang M Tsai, Holger Roth, Ming-Shiang Wu, Wei-Chih Liao,
Weichung Wang; Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a
retrospective study with cross-racial external validation.
Wilson Bakasa and SerestinaViriri; Pancreatic Cancer Survival Prediction: A Survey of the State-of the-Art.
Yasukuni Mori, Hajime Yokota, Isamu Hoshino, Yosuke Iwatate, Kohei Wakamatsu, Takashi Uno & Hiroki
Suyari1Suyari; Deep learning- based gene selection in comprehensive gene analysis in pancreatic cancer.
Guimin Dong, Mehdi Boukhechba, Kelly M. Shaffer, Lee M. Ritterband, Daniel G. Gioeli, Matthew J. Reilley;
Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.
Shanjida Khan Maliha, Romana Rahman Ema, Simanta Kumar Ghosh, Helal Ahmed, Md. Rafsun Jony Mollick,
Tajul Islam; Cancer Disease Prediction Using Naive Bayes K-Nearest Neighbour and J48 algorithm.
Khouloud Fakhfakh, Ahmed Maalel and Waad Farhat; Towards a Pancreatic Lesions Disease Classification
System based on Ontologies.
Qiuliang Yan, Dandan Hu, Maolan Li, Yan Chen; The Serum MicroRNA Signatures for Pancreatic Cancer
Detection and Operability Evaluation.
Behrouz Alizadeh Savareh, Hamid Asadzadeh Aghdaie, Ali Behmanesh, Azadeh Bashiri, Amir Sadeghi; A
machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA
signatures.
Dingwen Zhang, Jiajia Zhang, Qiang Zhang, Jungong Han, Shu Zhang, JunweiHan; Automatic Pancreas
Segmentation Based on Lightweight DCNN Modules and Spatial Prior Propagation.