Evaluation of feature selection methods utilizing random forest and logistic regression for lung tissue categorization using HRCT images

Abstract

Categorization of lung tissue patterns with interstitial lung diseases (ILD) utilize high‐resolution computed tomography (HRCT) lung images of the TALISMAN dataset which is challenging due to high intra‐class variation and inter‐class ambiguity. To tackle this, major contributions are made in three aspects. First, a novel shape‐based feature is proposed to quantify the amount of fibrotic and nodular components in a lung tissue pattern which helps to minimize intra‐class variation and inter‐class ambiguity. Second, we address the curse of dimensionality which often arises due to huge feature space. Third, to prevent an overfitting issue, the Grid Search optimization algorithm is utilized by tuning the Random Forest hyper‐parameters. In this manuscript, a framework is proposed to categorize lung tissue patterns by integrating four types of feature domains (a) intensity‐based, (b) texture‐based, (c) wavelet‐based, and (d) shape‐based along with the novel shape‐based feature. As a result, we encounter a large feature space (i.e., ), which leads to high dimensionality. To address this issue, we reduce the feature space using filter‐based f‐statistic, reliefF, minimum Redundancy Maximum Relevance (mRMR), and embedded‐based decision trees, regularization models. We found that the regularization model shrinks the feature space by 2.5 times in just 90 s whereas mRMR methods reduce the feature space by 10 times in 13 min. Using the proposed feature set, we employ Random Forest and Logistic Regression as potential classifiers to classify lung tissue patterns. Experiential results reveal that the proposed framework categorizes lung tissue patterns more effectively than state‐of‐the‐art hand‐crafted and deep learning‐based approaches.