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Licensed Unlicensed Requires Authentication Published by De Gruyter March 16, 2022

Classification of breast cancer with deep learning from noisy images using wavelet transform

Enes Cengiz, Muhammed Mustafa Kelek ORCID logo, Yüksel Oğuz ORCID logo and Cemal Yılmaz ORCID logo

Abstract

In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PSNR values. The best PSNR values were carried out with the Gaussian noise type. After that, the denoised images were classified by Convolution Neural Network (CNN), one of the deep learning techniques. In this classification process, the proposed CNN model and the VggNet-16 model were used. According to the classification result, better results were obtained with the proposed CNN model than VggNet-16. The best performance (86.9%) was obtained from the data set created Gaussian noise with 0.3 noise intensity.


Corresponding author: Enes Cengiz, Department of Mechatronic Engineering, Afyon Kocatepe University, Afyonkarahisar, 03200, Turkey, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: No ethical approval is required for this study.

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Received: 2021-05-25
Accepted: 2022-03-01
Published Online: 2022-03-16
Published in Print: 2022-04-26

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