Segmentation and Classification of Interstitial Lung Diseases Based on Hybrid Deep Learning Network Model

Abstract

Interstitial lung diseases (ILD) are diverse diseases that share pathological, radiological, and clinical traits and involve interstitial fibrosis and inflammation. These have a significant impact on lung disease morbidity and mortality. From the lung High-Resolution Computed Tomography (HRCT) image, the region of interest (ROI) had to be manually identified for most of the early ILD classification investigations, which was time-consuming. Additionally, the clinical signs of various disorders are identical, which makes precise detection difficult. In recent studies, outstanding results were achieved in categorizing medical photos using deep learning techniques. For ILD classification, a hybrid deep learning network model has been developed in this research. The lung portion of the HRCT images was initially segmented using an improved U-Net++ model. The multi-scale improved U-Net++ module has been applied for effective lung segmentation with lung anomalies. The segmented lung image's features were extracted for categorization in the second stage using a Refined Attention Pyramid Network (RAPNet). Then, we developed a MobileUNetV3 to classify five ILD classes. The ILD database is used to test the proposed approach. Due to the stage-by-stage improvement in the DL method performance, the proposed hybrid deep learning network model's performance has significantly increased.