Regional Context-sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Pattern of Diffuse Interstitial Lung Disease

The study proposes the use of a context-sensitive support vector machine (csSVM) to enhance the identification of diffuse interstitial lung disease (DILD) in high-resolution computerized tomography (HRCT) images. The csSVM simultaneously uses the decision value of each class and information from neighboring regions to classify an ROI, resulting in significantly higher accuracy for ROI and whole lung classification than the conventional SVM classifier. The proposed method characterizes ROIs using 21 textual and shape features and was validated using 900 ROIs with fivefold cross-validation and 84 whole lung images categorized by a radiologist. The study concludes that the csSVM provides better overall quantification of DILD. Additionally, the study mentions that it affected the development of AVIEW Lung Texture, which may imply that the csSVM was implemented or used as a basis for the software's development.