  Lung cancer is the commonest cause of cancer deaths worldwide, and its
mortality can be reduced significantly by performing early diagnosis and
screening. Since the 1960s, driven by the pressing needs to accurately and
effectively interpret the massive volume of chest images generated daily,
computer-assisted diagnosis of pulmonary nodule has opened up new opportunities
to relax the limitation from physicians' subjectivity, experiences and fatigue.
And the fair access to the reliable and affordable computer-assisted diagnosis
will fight the inequalities in incidence and mortality between populations. It
has been witnessed that significant and remarkable advances have been achieved
since the 1980s, and consistent endeavors have been exerted to deal with the
grand challenges on how to accurately detect the pulmonary nodules with high
sensitivity at low false-positives rate as well as on how to precisely
differentiate between benign and malignant nodules. There is a lack of
comprehensive examination of the techniques' development which is evolving the
pulmonary nodules diagnosis from classical approaches to machine
learning-assisted decision support. The main goal of this investigation is to
provide a comprehensive state-of-the-art review of the computer-assisted
nodules detection and benign-malignant classification techniques developed over
3 decades, which have evolved from the complicated ad hoc analysis pipeline of
conventional approaches to the simplified seamlessly integrated deep learning
techniques. This review also identifies challenges and highlights opportunities
for future work in learning models, learning algorithms and enhancement schemes
for bridging current state to future prospect and satisfying future demand.
