Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Hanife Avcı; Jale Karakaya

Makale detayları

Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Yazarlar:

Hanife Avcı | Jale Karakaya

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Artificial Intelligence | Industrial Biotechnology

Dergi:

Journal of Advanced Research in Natural and Applied Sciences Vol.9 Issue 2, pp.345-354

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291656

pISSN:

2757-5195

Anahtar kelimeler:

Özet:

 Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis CAD models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods Ⅰ. Parameter values are Gauss filter 3, Laplacian filter 0.6 and 0.6; Ⅱ. Parameter values are Gauss filter 1, Laplacian filter 2 and 2. In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

 Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis CAD models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods Ⅰ. Parameter values are Gauss filter 3, Laplacian filter 0.6 and 0.6; Ⅱ. Parameter values are Gauss filter 1, Laplacian filter 2 and 2. In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

 Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis CAD models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods Ⅰ. Parameter values are Gauss filter 3, Laplacian filter 0.6 and 0.6; Ⅱ. Parameter values are Gauss filter 1, Laplacian filter 2 and 2. In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

 Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis CAD models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods Ⅰ. Parameter values are Gauss filter 3, Laplacian filter 0.6 and 0.6; Ⅱ. Parameter values are Gauss filter 1, Laplacian filter 2 and 2. In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

Makale detayları

Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Yazarlar:

Hanife Avcı | Jale Karakaya

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Artificial Intelligence | Industrial Biotechnology

Dergi:

Journal of Advanced Research in Natural and Applied Sciences Vol.9 Issue 2, pp.345-354

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291656

pISSN:

2757-5195

Computer-Assisted

image enhancement

image processing

machine learning

classification

Sayı

Makale detayları

Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Yazarlar: Hanife Avcı | Jale Karakaya

Yayıncı: Çanakkale Onsekiz Mart University

Ana Başlıklar: Artificial Intelligence | Industrial Biotechnology

Dergi: Journal of Advanced Research in Natural and Applied Sciences Vol.9 Issue 2, pp.345-354

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001291656

pISSN: 2757-5195

Computer-Assisted

image enhancement

image processing

machine learning

classification

Sayı

Yazarlar:

Hanife Avcı | Jale Karakaya

Yayıncı:

Çanakkale Onsekiz Mart University

Ana Başlıklar:

Artificial Intelligence | Industrial Biotechnology

Dergi:

Journal of Advanced Research in Natural and Applied Sciences Vol.9 Issue 2, pp.345-354

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001291656

pISSN:

2757-5195