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

Interstitial lung diseases (ILD) are a diverse group of 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. The region of interest (ROI) from the lung High-Resolution Computed Tomography (HRCT) picture 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. Outstanding results were achieved in categorizing medical photos using deep learning techniques in recent studies. 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. For effective lung segmentation with lung anomalies, the multi-scale improved U-Net++ module has been applied. The segmented lung image’s features were extracted for categorization in the second stage using a Refined Attention Pyramid Network (RAPNet). Then, for the classification of five ILD classes, we developed an Improved MobileUNetV3. The ILD database is used to test the proposed approach. Due to the stage-by-stage improvement in DL method performance, the proposed hybrid deep learning network model's performance has improved dramatically.