Breast Cancer Detection

Introduction:

Artificial Intelligence (AI) has achieved remarkable performance in medical image analysis, particularly in tasks such as object detection, segmentation, and classification. In this paper, we introduce a solution for automatic breast cancer diagnosis based on the U-Net architecture, which we call (U-Net)+. The novel (U-Net)+ is designed to handle both segmentation and classification tasks within a single framework. We retained the original U-Net architecture due to its strong learning capabilities and advantages in semantic segmentation. Notably, we incorporated fully connected layers into the bottleneck layers, serving as a multi-functional classifier for both initial diagnoses based on raw images and further diagnoses for segmented images. The (U-Net)+ model is trained using a joint loss function. We conducted experiments on breast ultrasound images, demonstrating that the (U-Net)+ performs well in both classification and segmentation tasks.

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