Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting Method

Abstract

Lung carcinoma, which is commonly known as lung cancer, is one of the most common cancers throughout the world. Mostly, it is not diagnosed until it has spread, and it is very difficult to treat. Hence, early diagnosis of benign and malignant pulmonary nodules can help in the risk assessment of lung cancer for patients, and with proper treatment can save their lives. In this study, a framework for the classification of pulmonary nodules from Computerized Tomography (CT) images using the machine learning-based modified gradient boosting method is proposed. Initially, the obtained CT scan images are preprocessed for better image quality. Next, a random walker method is used to segment the lung nodule boundaries based on seeds provided by the user. After that, the intensity and texture features are extracted using the Local Binary Pattern (LBP) filter and the coefficients of the Riesz wavelet transform. Finally, the proposed modified gradient boost classifier model is trained and tested using the extracted features to classify nodules as either benign or malignant. The proposed framework is verified and validated using the Lung Image Database Consortium (LIDC-IDRI) dataset. From the performance analysis, it was observed that the proposed method achieves a precision, recall, F1 score, and validation accuracy of 0.957, 0.91, 0.941, and 95.67%, respectively. The performance of the proposed method is compared with existing models and is found to be superior. It was found that the proposed classifier is able to efficiently classify pulmonary nodules as either benign or malignant.