Medical Image Segmentation Using Double U-Net And Deep Learning

Abstract

Image segmentation is crucial in various fields, including medical diagnostics, where precision is paramount. This paper proposes a convolutional neural network (CNN)- based model for segmenting nuclei in medical images. By leveraging CNNs' ability to extract hierarchical features, the model detects subtle patterns indicative of nuclei, enhancing segmentation accuracy and capturing morphological variations. Incorporating edge detection further refines segmentation by highlighting boundaries. The proposed model generates a mask image outlining segmented regions, ensuring consistency and reliability. Experimental validation demonstrates its superiority in prediction performance and computational efficiency over existing methods. The Double U-Net architecture effectively balances global context and fine-grained details. Overall, this model offers a sophisticated yet pragmatic approach to medical image segmentation, fostering advancements in diagnostic accuracy and patient care.