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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 8 Sayı: 1

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

Fatih Gül(Recep Tayyip Erdoğan Üniversitesi)0000-0001-5072-2122
Muhammed Uçar(RECEP TAYYIP ERDOGAN UNIVERSITY)0009-0007-0445-6637
Nur Hürsoy(RECEP TAYYIP ERDOGAN UNIVERSITY)0000-0001-5059-2268
Pages
185–190
Publication Date
2025-01-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving.
📊
Key Findings & Data:The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased.
💡
Conclusion & Impact:The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Mammography · Image processing · Resolution enhancement

Abstract

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.

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.

🔖

Cite This Article

Gül, F., Uçar, M., Hürsoy, N. (2025). Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer. Black Sea Journal of Engineering and Science, Cilt: 8 Sayı: 1, 185–190. https://doi.org/10.34248/bsengineering.1535503

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References (12)

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