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

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

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
Publication statusPublished - May 2019
Event2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 - Chengdu, China
Duration: May 25 2019May 28 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
Period5/25/195/28/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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