Detection of Idiopathic Pulmonary Fibrosis Lesion Area Based on Transfer Learning

JieLin Xue, Tian Pu, Lu Guo, Zhenming Peng, Xing He, Yicen Han

Abstract: Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease that significantly impacts individuals' health. Early diagnosis of IPF can enhance patient survival rates. Considering the limited research on automatic detection of IPF in academic circles and the scarcity of labeled data, this paper proposes a transfer learning-based algorithm for detecting IPF lesion areas. The proposed method consists of two stages: firstly, construct an IPF-like dataset comprising natural texture images with similar visual features to IPF, and pre-train the U-net network using this dataset; secondly, fine-tune the network using both the pre-trained weights and actual IPF data, followed by utilizing the trained network for lesion area detection. Experimental results demonstrate substantial improvements in terms of accuracy and sensitivity compared to corner distribution-based methods for IPF detection.