AUD PFS: ATTENTION U-NET BASED DEEP LEARNING FRAMEWORK FOR ACCURATE PULMONARY FIBROSIS SEGMENTATION IN MEDICAL IMAGES

Abstract

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive lung disorder characterized by irreversible scarring of lung parenchyma. High-resolution computed tomography (HRCT) is the imaging standard for IPF evaluation, but manual quantification of fibrotic lesions is tedious and subjective. We introduce AUD PFS, an Attention U-Net deep learning framework designed specifically for automated segmentation of pulmonary fibrosis patterns including honeycombing, reticulation, and ground-glass opacities. Attention gates suppress irrelevant background features and highlight diseased lung zones. Evaluated on multi-center HRCT datasets, the model achieves a Dice score of 89.4% and an Intersection-over-Union (IoU) of 81.3%.