Enhancing Breast Cancer detection accuracy using U Net architecture

The U Net deep learning architecture is used in this study’s ultrasound image based breast cancer detection system to achieve accurate picture segmentation. The first step in the process is picture collecting and pre processing, where methods like data augmentation, normalization, and noise reduction improve image quality and increase the generalizability of the model. The encoder decoder structure and skip connections of the U Net architecture allow for the precise and effective localization of malignant areas. The U Net model provides high precision segmentation by reducing false positives and collecting contextual information as well as fine characteristics. A dice loss function is used to optimize the system, guaranteeing strong model performance in locating and classifying breast cancer areas. The U Net model’s promise for clinical applications in breast cancer detection is highlighted by experimental results that demonstrate its ability to detect and segment malignant regions in ultrasound images with a decrease in training loss and an improvement in accuracy with time.