The Classification of Mammogram Using Convolutional Neural Network with Specific Image Preprocessing for Breast Cancer Detection

Hao Chun Lu*, El Wui Loh, Shih Chen Huang

*Corresponding author for this work

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

40 Scopus citations

Abstract

The incidence rate of breast cancer continued to rise in the last few decades. Current screening strategy of breast cancer is based on classic X-ray imaging. The sensitivity and specificity of the diagnosis are largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. The convolutional neural network is an effective technique for classification in deep learning model. In this study, we utilized median filter, contrast-limited adaptive histogram equalization, and data augmentation to preprocess over 9,000 mammograms, and trained a classified model by using convolutional neural network. The experiment results demonstrated that the accuracy of model with preprocessed images significantly outperformed the model without preprocessed images.

Original languageEnglish
Title of host publication2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-12
Number of pages4
ISBN (Electronic)9781728108315
DOIs
StatePublished - 05 2019
Externally publishedYes
Event2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 - Chengdu, China
Duration: 25 05 201928 05 2019

Publication series

Name2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019

Conference

Conference2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
Country/TerritoryChina
CityChengdu
Period25/05/1928/05/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • breast cancer detection
  • convolution neural network
  • deep learning
  • mammograms

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