Analysis of Deep Feature Extraction for Colorectal Cancer Detection

Devvi Sarwinda, Alhadi Bustamam, Radifa H. Paradisa, Terry Argyadiva, Wibowo Mangunwardoyo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Colorectal cancer is the most common cancer worldwide in the third. Detection of colon cancer is an essential task for the histopathologist as they have to analyze the morphology of the images at different magnifications. In this study, we classified benign and adenocarcinoma using 10000 images of benign colon tissue. We proposed a feature extraction method by the deep convolutional neural network. First, we learn the features of data from ResNet-50 and DenseNet-121. Then, we conduct colon cancer classification by popular classifiers such as SVM, Random Forest, K-Nearest Neighbor, and XGBoost. We evaluated our models on two kinds of testing data (25% and 15% of the whole dataset). In this research, the data was conducted on the Kaggle colon tissue dataset. The experimental results indicate that the extraction of features in DenseNet-121 based architecture leads to higher accuracy, sensitivity, and specificity of ResNet-50 architecture for all classifiers. DenseNet-121 gets about 98.53% and 98.63% with KNN classifier for accuracy and sensitivity, respectively.

Original languageEnglish
Title of host publicationICICoS 2020 - Proceeding
Subtitle of host publication4th International Conference on Informatics and Computational Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195261
DOIs
Publication statusPublished - 10 Nov 2020
Event4th International Conference on Informatics and Computational Sciences, ICICoS 2020 - Semarang, Indonesia
Duration: 10 Nov 202011 Nov 2020

Publication series

NameICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences

Conference

Conference4th International Conference on Informatics and Computational Sciences, ICICoS 2020
CountryIndonesia
CitySemarang
Period10/11/2011/11/20

Keywords

  • Colon cancer
  • Convolutional Neural Network
  • Deep Feature Extraction
  • DenseNet
  • ResNet

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