Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer

Fatih Gül; Muhammed Uçar; Nur Hürsoy

Makale detayları

Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer

Yazarlar:

Fatih Gül | Muhammed Uçar | Nur Hürsoy

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.185-190

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331048

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE Contrast Limited Adaptive Histogram Equalization algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

 Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE Contrast Limited Adaptive Histogram Equalization algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

 Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE Contrast Limited Adaptive Histogram Equalization algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

 Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE Contrast Limited Adaptive Histogram Equalization algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

Makale detayları

Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer

Yazarlar:

Fatih Gül | Muhammed Uçar | Nur Hürsoy

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.185-190

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331048

pISSN:

2619-8991

Mammography

Image processing

Resolution enhancement

Artificial intelligence

Clahe algorithmMammography

Image processing

Resolution enhancement

Artificial intelligence

Clahe algorithm

Sayı

Makale detayları

Yazarlar: Fatih Gül | Muhammed Uçar | Nur Hürsoy

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Biyomedikal Görüntüleme

Dergi: Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.185-190

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001331048

pISSN: 2619-8991

Sayı

Yazarlar:

Fatih Gül | Muhammed Uçar | Nur Hürsoy

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Görüntüleme

Dergi:

Black Sea Journal of Engineering and Science Vol.8 Issue 1, pp.185-190

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001331048

pISSN:

2619-8991