Abstract
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.
Highlights
Cancer is a worldwide disease that ranks as the second reason for death
The proposed method achieved a sensitivity of 92% and area under the curve (AUC) of 0.95 when evaluated on LIDC-IDRI
The convolutional Neural Networks (CNNs) model achieved the best performance with an accuracy, sensitivity, and specificity of 84.15%, 83.96%, and 84.32%, respectively
Summary
Cancer is a worldwide disease that ranks as the second reason for death. Statistics show there are. The incidence rates of lung cancers have declined annually, and the trend of decline is accelerating. Early detection of lung nodules on lung CT to the Traditionally, clinicians observe, analyze, and interpret the lesion information according scans is of great significance for the successful diagnosis and treatment of lung cancer. The interpretation of each lesion by radiologists is a complicated the results of nodule morphology and clinical conditions [3]. The automatic mode is needed, images, reduce and data exploit nodulean that even experienced chestwhich radiologists can help clinicians analyze CT images, reduce the workload, identify and exploit the nodule that even may miss, and increase the accuracy of diagnosis [3]. Trend incidence rates rates for cancers in the States from to 2013
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