A Hybrid Model for the Segmentation of Mammogram Images using Otsu Thresholding, Morphology and U-Net
Vandana Saini, Meenu Khurana, Rama Krishna Challa · Biomedical & Pharmacology Journal · 2025
Mammogram image segmentation is crucial for early detection and treatment of breast cancer. Timely detection can help in saving the patient’s life. By accurately identifying and isolating regions of interest in mammograms, we can improve diagnostic accuracy. In this paper a hybrid model for segmentation using Ostu thresholding with morphological operations and U-Net model is proposed for accurate segmentation of mammogram images. The incorporation of attention mechanisms and residual connections in U-Net helps in enhancing the model’s performance. The proposed model performs better than recent existing models, achieving high precision, recall, F1 score, accuracy, and area under curve (AUC). The proposed model is evaluated on the MIAS dataset and achieved an F1 score of 0.9764, precision of 0.9802, recall of 0.9980, accuracy of 0.9902, and an AUC of 0.99997. These results had shown significant improvements in comparison with existing models, making it a suitable and accurate model for the early detection and diagnosis of breast cancer.