Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.289044

Keywords:

breast cancer, ultrasound imaging, deep learning, UNet, image segmentation, augmentation

Abstract

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.

Author Biographies

Assel Mukasheva, Kazakh-British Technical University

PhD, Associate Professor

School of Information Technology and Engineering

Dina Koishiyeva, Almaty University of Power Engineering and Telecommunications; Satbayev University

Master’s Student

Department of Information Systems and Cybersecurity

PhD Doctorate

Department of Cybersecurity, Information Processing and Storage

Zhanna Suimenbayeva, Satbayev University

PhD Doctorate

Department of Cybersecurity, Information Processing and Storage

Sabina Rakhmetulayeva, International Information Technology University

PhD, Associate Professor

Department of Information Systems

Aigerim Bolshibayeva, International Information Technology University

PhD, Assistant Professor

Department of Information Systems

Gulnar Sadikova, Almaty University of Power Engineering and Telecommunications

PhD Doctorate

Department of Telecommunications and Space Engineering

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Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

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Published

2023-10-31

How to Cite

Mukasheva, A., Koishiyeva, D., Suimenbayeva, Z., Rakhmetulayeva, S., Bolshibayeva, A., & Sadikova, G. (2023). Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image. Eastern-European Journal of Enterprise Technologies, 5(9 (125), 85–97. https://doi.org/10.15587/1729-4061.2023.289044

Issue

Vol. 5 No. 9 (125) (2023): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2023 Assel Mukasheva, Dina Koishiyeva, Zhanna Suimenbayeva, Sabina Rakhmetulayeva, Aigerim Bolshibayeva, Gulnar Sadikova

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