Faculty Publications
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Item A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans(Elsevier Ltd, 2019) Savitha, G.; Padikkal, P.A fully-automated computer-aided detection (CAD) system is being proposed in this paper for identification and classification of subsolid lung nodules present in Computed Tomography(CT) scans. The system consists of two stages. The first stage aims at detecting locations of the nodules, while the second one classifies the same into the solid and subsolid category. The system performs segmentation of the region of interest (ROI) and extraction of relevant features from the segmented ROI. Graylevel covariance matrix (GLCM) is being used to extract the Feature vectors. Principle component analysis (PCA) algorithm is used to select significant features in the feature space formed by the vectors. The nodule localization is performed using support vector machine, fuzzy C-means, and random forest classification algorithms. The identified nodules are further grouped into solid and subsolid nodules by extracting histogram of gradient (HoG) features adopting K-means and support vector machine (SVM) classifiers. A large number of annotated images from the widely available benchmark database is tested to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using the relevant quantitative measures. The developed CAD system is found to identify subsolid nodules with a high percentage of accuracy. © 2019 Elsevier LtdItem A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans(Elsevier Ltd, 2020) Savitha, G.; Padikkal, P.Prompt detection of malignant lung nodules significantly improves the chance of survivability of the affected patients. The lung nodules in their early stages appear as subsolid or part-solid nodules whose identification remains a challenging task. Many of the present lung nodule detection systems fail to identify the nodules in their early stages. Limitations in the feature extraction process lead to significant false-positive rates, which eventually diminish the accuracy aspects of the system. In this study, a sophisticated deep learning approach is employed for feature extraction which improves the nodule localization or identification stage of the system. Further, the false positives sneaking out of the system are drastically reduced by adopting a Conditional Random Framework in the model. The quantitative demonstrations prove the efficiency of the model to detect sub-solid nodules in CT images. Thus the employability of the model for early detection of the nodules is tested and verified. © 2020 Elsevier Ltd