Breast Cancer Detection Using Image Denoising and UNet Segmentation for Mammography Images


  • M. Subha, P. Srimanchari


Breast Cancer, DenseNet, Mammography, NLW, VGG19, WaterUNet


Breast cancer remains a significant global health concern, necessitating the development of effective risk assessment for detection and prevention.  It is a worldwide health issue that requires enhanced early detection methods. In this paper represents breast cancer detection technique using advanced learning techniques. Initially the dataset has collected form benchmark datasets as Mammography with Benign Malignant Masses with three categories like MIAS, Inbreast and DDSM. The Dataset has trained using VGG19 with Modified Densenet. The Image Denoising has used Non local mean with Wavelet (NLW) Algorithm. NLW denoising minimizes noise and preserves features, guaranteeing high-quality images for analysis. Image segmentation has done using WaterUNet model. WaterUNet model is correctly identifies malignant spots in mammography images. Finally, the classification has done with VGG19 algorithm with 99% accuracy. Experimental results show that the proposed technique outperforms with existing approaches like accuracy, precision, recall and f-measure.


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How to Cite

M. Subha. (2024). Breast Cancer Detection Using Image Denoising and UNet Segmentation for Mammography Images. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3015–3029. Retrieved from



Research Article