English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Lung cancer is the commonest cause of cancer deaths worldwide, and itsmortality can be reduced significantly by performing early diagnosis andscreening. Since the 1960s, driven by the pressing needs to accurately andeffectively interpret the massive volume of chest images generated daily,computer-assisted diagnosis of pulmonary nodule has opened up new opportunitiesto relax the limitation from physicians' subjectivity, experiences and fatigue.And the fair access to the reliable and affordable computer-assisted diagnosiswill fight the inequalities in incidence and mortality between populations. Ithas been witnessed that significant and remarkable advances have been achievedsince the 1980s, and consistent endeavors have been exerted to deal with thegrand challenges on how to accurately detect the pulmonary nodules with highsensitivity at low false-positives rate as well as on how to preciselydifferentiate between benign and malignant nodules. There is a lack ofcomprehensive examination of the techniques' development which is evolving thepulmonary nodules diagnosis from classical approaches to machinelearning-assisted decision support. The main goal of this investigation is toprovide a comprehensive state-of-the-art review of the computer-assistednodules detection and benign-malignant classification techniques developed over3 decades, which have evolved from the complicated ad hoc analysis pipeline ofconventional approaches to the simplified seamlessly integrated deep learningtechniques. This review also identifies challenges and highlights opportunitiesfor future work in learning models, learning algorithms and enhancement schemesfor bridging current state to future prospect and satisfying future demand.

journal of cancer research and clinical oncology

Evolving the pulmonary nodules diagnosis from classical approaches to  deep learning aided decision support: three decades development course and  future prospect