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

Year 2025, , 185 - 190, 15.01.2025

Fatih Gül , Muhammed Uçar , Nur Hürsoy

https://doi.org/10.34248/bsengineering.1535503

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.

Keywords

Mammography, Image processing, Resolution enhancement, Artificial intelligence, Clahe algorithm

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

Thanks

This article was produced from the thesis of the second author, supervised by the first and co-supervised by the third author, a radiology specialist with expertise in mammography. All of the research accomplished in “Artificial Intelligence/Internet of Things Research Laboratory (AIIoT Lab), Recep Tayyip Erdogan University”, supported under the TÜBİTAK-121E544 project. The authors thank TÜBİTAK for contributions.

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