Research Article

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

Volume: 41 Number: 4 December 29, 2020
EN

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

Abstract

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Publication Date

December 29, 2020

Submission Date

February 20, 2020

Acceptance Date

November 29, 2020

Published in Issue

Year 2020 Volume: 41 Number: 4

APA
Uzunhisarcıklı, E., Göreke, V., & Sarı, V. (2020). Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. Cumhuriyet Science Journal, 41(4), 968-975. https://doi.org/10.17776/csj.691683
AMA
1.Uzunhisarcıklı E, Göreke V, Sarı V. Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. CSJ. 2020;41(4):968-975. doi:10.17776/csj.691683
Chicago
Uzunhisarcıklı, Esma, Volkan Göreke, and Vekil Sarı. 2020. “Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images”. Cumhuriyet Science Journal 41 (4): 968-75. https://doi.org/10.17776/csj.691683.
EndNote
Uzunhisarcıklı E, Göreke V, Sarı V (December 1, 2020) Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. Cumhuriyet Science Journal 41 4 968–975.
IEEE
[1]E. Uzunhisarcıklı, V. Göreke, and V. Sarı, “Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images”, CSJ, vol. 41, no. 4, pp. 968–975, Dec. 2020, doi: 10.17776/csj.691683.
ISNAD
Uzunhisarcıklı, Esma - Göreke, Volkan - Sarı, Vekil. “Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images”. Cumhuriyet Science Journal 41/4 (December 1, 2020): 968-975. https://doi.org/10.17776/csj.691683.
JAMA
1.Uzunhisarcıklı E, Göreke V, Sarı V. Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. CSJ. 2020;41:968–975.
MLA
Uzunhisarcıklı, Esma, et al. “Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images”. Cumhuriyet Science Journal, vol. 41, no. 4, Dec. 2020, pp. 968-75, doi:10.17776/csj.691683.
Vancouver
1.Esma Uzunhisarcıklı, Volkan Göreke, Vekil Sarı. Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images. CSJ. 2020 Dec. 1;41(4):968-75. doi:10.17776/csj.691683

Cited By

As of 2026, Cumhuriyet Science Journal will be published in six issues per year, released in February, April, June, August, October, and December