Deep learning the features maps for automated tumor grading of lung nodule structures using convolutional neural networks

Abstract

Accurately identifying the exact boundary region of the pulmonary nodules in lung cancer images are the most challenging tasks in the Computer Aided Diagnosing schemes (CADx). Detecting the boundaries from different nodule structures is crucial due to the presence of similar visualization characteristics between the nodules and its surroundings. The study proposed an approach for pulmonary nodule region of interest (NROI) detection and segmentation using Computed Tomography (CT) lung images. Lung nodule CT images are acquired from the Lung Image Database Consortium (LIDC) public repository having 1018 cases. In this paper, a methodology for automated tumor grading of pulmonary lung nodules is proposed using Convolutional Neural Network (CNN). The salient features of benign and malignant nodules from different nodule structures are automatically self-learned and classified based on the classification strategy. The stages involved in the methodology are: 1) Pre-processing the image datasets using discrete wavelet transforms (DWT). 2) NROI segmentation. 3) NROI Feature extraction using CNN. 4) Nodule classification. CNN are trained with self-learned extracted features from NROI and are further classified as benign or malignant. Analyzing and segregating these extracted features plays a vital role in the correct classification of malignancy levels. The methodology is compared with conventional state-of-art methods and traditional hand-crafted methods. A total of 710 pulmonary nodules are used in the study, with 258 benign samples and 452 malignant samples. A consistent behavior was observed using CNN with reduced low false positives and a classification accuracy of 96.5%, sensitivity of 96%, specificity of 96.55% and standard Receiver operating characteristic (ROC) curve with the highest value of 0.969 was recorded.