Development of a Computer-Aided Differential Diagnosis System to Distinguish Between Usual Interstitial Pneumonia and Non-specific Interstitial Pneumonia Using Texture- and Shape-Based Hierarchical Classifiers on HRCT Images.
SangHoon Jun, BeomHee Park, Joon Beom Seo, SangMin Lee, Namkug Kim
This study presents a computer-aided differential diagnosis (CADD) system for differentiating usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) using high-resolution computed tomography (HRCT) images. Six local interstitial lung disease patterns were identified, and a support vector machine (SVM) classifier was employed to label regions of interest based on texture and shape characteristics. The developed system's performance was then tested on 54 clinically diagnosed UIP and NSIP patients' HRCT images. Impressively, the CADD system achieved an accuracy of up to 91% after sequential-forward feature selection, outperforming thoracic radiologists whose accuracies ranged from 75% to 87%. The findings from this study later were transferred to the development of AVIEW Lung Texture by Coreline Soft, embodying these advanced diagnostic capabilities into commercial software to potentially revolutionize diagnostic radiology.