Hybrid Radiomic-HOG Ensemble Model for Accurate Pulmonary Nodule Diagnosis.

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Abstract

Lung cancer remains one of the deadliest forms of cancer worldwide, making early and accurate pulmonary-nodule classification essential for improving patient prognosis. This study presents a robust ensemble-stacking framework that integrates Histogram of Oriented Gradients with advanced radiomic features to distinguish benign from malignant nodules. Experiments were conducted on the publicly available LIDC-IDRI dataset, which comprises of 1,018 thoracic computed tomography scans with expert-annotated nodules. Complementary feature sets capturing both local edge patterns and high-order texture and shape descriptors were extracted. On this feature set, Random Forest, Logistic Regression, and Support Vector Machine served as base learners. Through extensive hyperparameter tuning and class-balanced training, followed by 5-fold cross-validation, the proposed ensemble achieved an accuracy of 93.26%, a sensitivity of 90.76%, and an AUC-ROC of 97.96%, outperforming individual feature-only models and several recent state-of-the-art approaches. Furthermore, feature-importance analysis highlights the importance of morphological descriptors and the complementary value of gradient-based features. These results demonstrate that integrating different imaging biomarkers within an ensemble framework can significantly enhance diagnostic performance. Future work will extend this framework to multi-modal imaging and also to incorporate semi-supervised learning to reduce manual label dependence and improve the overall generalisation.

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