Comparative study of various deep convolutional neural networks in the early prediction of cancer

J. Andrew, Rex Fiona, H. Caleb Andrew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In the recent decades, cancer has become a major cause of mortality worldwide. Predicting cancer cells and tumors at the early stages can be treated. Computer-aided diagnosis systems are used to analyze the MRI and CT scan images. However, it is inefficient to predict the disease as it works with high-level image features. It is important to extract the low-level feature of the image details in order to improve the prediction accuracy. Deep learning models are efficient in extracting the low-level image features. A convolutional neural network (CNN) is one of the popular deep learning architectures efficient in feature extraction. In this paper, the various types of CNN models are discussed. A comparative study of different CNN models along with segmentation and classification models are discussed. Finally, the prediction accuracy of CNN architectures with their dataset details are analyzed.

Original languageEnglish
Title of host publication2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages884-890
Number of pages7
ISBN (Electronic)9781538681138
DOIs
Publication statusPublished - 05-2019
Event2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019 - Madurai, India
Duration: 15-05-201917-05-2019

Publication series

Name2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019

Conference

Conference2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019
Country/TerritoryIndia
CityMadurai
Period15-05-1917-05-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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