Abstract
Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.
Highlights
Lung cancer is becoming one of the main threats to human health at present in the world
The stages of feature extraction and nodule classification belong to the false-positive reduction step
The classifiers are supervised learning approaches in machine learning domain, such as SVM, k-nearest neighbor (k-NN), artificial neural networks (ANNs), and decision tree which have been used in lung nodule classification [22]
Summary
Lung cancer is becoming one of the main threats to human health at present in the world. Typical CAD systems for cancer detection and diagnosis (i.e., breast, lung, and polyp) cover four stages as depicted, including candidate nodule ROI (Region of Interest) detection, feature extraction, and nodule classification. The stages of feature extraction and nodule classification belong to the false-positive reduction step. Current CAD schemes for nodule characterization have achieved high sensitivity levels and would be able to improve radiologists’ performance in the characterization of nodules in thin-section CT, whereas current schemes for nodule detection appear to report many false positives. The false-positive reduction step, or classification step, the aim of which is to learn a system capable of the prediction of the unknown output class of a previously unseen suspicious nodule with a good generalization ability, is a critical part in the lung nodule detection system.
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