2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 360-368, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Taha Y. Abdulqader 1 * , Kifaa Hadi Thanoon 2 , Shatha A. Baker 3
Doi: https://doi.org/10.54216/JISIoT.170223
When using mammography to diagnose breast cancer, segmenting medical scans is a crucial step. Accurate segmentation facilitates early diagnosis, which in turn makes it possible to administer individualized treatment plans, ultimately improving patient outcomes. However, for these Deep Learning (DL) models to be trained efficiently and perform optimally, they require access to large datasets. The lack of sufficient photographs in many publicly available datasets to adequately train deep learning models is a common flaw. Therefore, this work aims to examine the effects of various affine data augmentations on the Dice Score of a U-NET model utilizing a recently released public dataset of Contrast-Enhanced Spectral Mammography (CESM) images. The collection consists of 1003 CESM images and matching segmentation masks made by a certified radiologist. Modifying certain model parameters on the CESM dataset and investigating the impact of single and combination data augmentations on the model's overall performance are the objectives of the study. Images that were moved in the x-direction and sheared vertically were used to train the best-performing model. On the test set, the model's Dice Score was 56.6%, which was 9% better than the baseline result and showed how crucial data augmentation is when working with small datasets.
Deep Learning , Contrast-Enhanced Spectral Mammography , Mammograms , U-NET
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