Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder

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Author(s): Maira Araujo de Santana (Universidade Federal de Pernambuco, Brazil), Jessiane Mônica Silva Pereira (Universidade de Pernambuco, Brazil), Washington Wagner Azevedo da Silva (Universidade Federal de Pernambuco, Brazil)and Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil)
Copyright: 2021
Pages: 16
Source title: AI Innovation in Medical Imaging Diagnostics
Source Author(s)/Editor(s): Kalaivani Anbarasan (Department of Computer Science and Engineering, Saveetha School of Engineering, India & Saveetha Institute of Medical and Technical Sciences, Chennai, India)
DOI: 10.4018/978-1-7998-3092-4.ch004

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

In this chapter, the authors used autoencoder in data preprocessing step in an attempt to improve image representation, consequently increasing classification performance. The authors applied autoencoder to the task of breast lesion classification in mammographic images. Image Retrieval in Medical Applications (IRMA) database was used. This database has a total of 2,796 ROI (regions of interest) images from mammograms. The images are from patients in one of the three conditions: with a benign lesion, a malignant lesion, or presenting healthy breast. In this study, images were from mostly fatty breasts and authors assessed different intelligent algorithms performance in grouping the images in their respective diagnosis.

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Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder

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