BACKGROUND AND OBJECTIVES: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance. METHODS: Numerous methods have been proposed to classify malignancy, but there are not many studies that facilitate nodule characteristics in classification steps. In this study, we use information on nodule characteristics and propose cascaded classification schemes. A group of hand-crafted features and deep features are used to define the nodules. In the first step of the classifier, the nodule characteristics are classified based on individual base classifiers. In the second step, the results of the first level classifier are combined for use in malignancy classification. In addition, stacking methods are applied to improve the performance of the cascaded classifiers. RESULTS: The results confirmed that combining deep and hand-crafted features contribute to classification performance with an 8% improvement in average classification accuracy, 9% improvement in sensitivity, and 3% in specificity. Deep features from a nodule bounding area are more descriptive than the exact nodule region. The best performing cascaded classifier featured a classification accuracy of 84.70%, sensitivity of 67.37%, and specificity of 95.46%. First level stacking demonstrated similar results on classification accuracy and specificity but sensitivity was measured at 75.59%. Stacking on both levels provided the best classification accuracy and specificity with scores of 86.98% and 96.06%, respectively. When the malignancy ratings were grouped, stacking on both levels demonstrated better performance than other methods with a classification accuracy of 88.80%, sensitivity of 88.41%, and specificity of 94.12%. CONCLUSIONS: Information on cascading characteristics with image features is beneficial for the classification of the malignancy ratings. Stacking approaches on both levels demonstrate better classification accuracy, but in the context of sensitivity, first level stacking performs better. Grouping the malignancy ratings results in better classification outcomes as in the case of similar studies in the literature.
BACKGROUND AND OBJECTIVES: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance. METHODS: Numerous methods have been proposed to classify malignancy, but there are not many studies that facilitate nodule characteristics in classification steps. In this study, we use information on nodule characteristics and propose cascaded classification schemes. A group of hand-crafted features and deep features are used to define the nodules. In the first step of the classifier, the nodule characteristics are classified based on individual base classifiers. In the second step, the results of the first level classifier are combined for use in malignancy classification. In addition, stacking methods are applied to improve the performance of the cascaded classifiers. RESULTS: The results confirmed that combining deep and hand-crafted features contribute to classification performance with an 8% improvement in average classification accuracy, 9% improvement in sensitivity, and 3% in specificity. Deep features from a nodule bounding area are more descriptive than the exact nodule region. The best performing cascaded classifier featured a classification accuracy of 84.70%, sensitivity of 67.37%, and specificity of 95.46%. First level stacking demonstrated similar results on classification accuracy and specificity but sensitivity was measured at 75.59%. Stacking on both levels provided the best classification accuracy and specificity with scores of 86.98% and 96.06%, respectively. When the malignancy ratings were grouped, stacking on both levels demonstrated better performance than other methods with a classification accuracy of 88.80%, sensitivity of 88.41%, and specificity of 94.12%. CONCLUSIONS: Information on cascading characteristics with image features is beneficial for the classification of the malignancy ratings. Stacking approaches on both levels demonstrate better classification accuracy, but in the context of sensitivity, first level stacking performs better. Grouping the malignancy ratings results in better classification outcomes as in the case of similar studies in the literature.