Enhanced U-Net Architecture with VGG16 Backbone and Attention Mechanisms for Automated Nasal Sinus Disease Segmentation

Abstract

This paper proposes an automated nasal sinus disease segmentation system using an enhanced U-Net model with VGG16 backbone and attention mechanism. It would overcome the weaknesses inherent in manual segmentation by presenting a superior and accurate solution. The data set for this research consists of medical image data, public and self-collected, preprocessed for segmentation purposes. Preprocessing pipeline included data annotation using Roboflow, resizing, normalization, and binary mask creation. Random rotation, flipping, and shifting were some of the data augmentation methods used to make the model more robust. The proposed model architecture includes a VGG16-based encoder with a bottleneck for enhancing features and a decoder with attention gates to achieve maximum attention in regions of interest in disease areas. Adam optimizer and binary cross-entropy loss were used for training the model and tested on Dice coefficient, Intersection over Union (IoU), and F1-score. The model underwent great improvement after 50 epochs with accuracy 98.43%, which indicates its proficiency in nasal sinus disease segmentation. Furthermore, the proposed model was compared with other existing segmentation models, and the results demonstrated its superior performance, justifying the effectiveness of the custom architecture.