  Lung cancer has been one of the most prevalent disease in recent years.
According to the research of this field, more than 200,000 cases are identified
each year in the US. Uncontrolled multiplication and growth of the lung cells
result in malignant tumour formation. Recently, deep learning algorithms,
especially Convolutional Neural Networks (CNN), have become a superior way to
automatically diagnose disease. The purpose of this article is to review
different models that lead to different accuracy and sensitivity in the
diagnosis of early-stage lung cancer and to help physicians and researchers in
this field. The main purpose of this work is to identify the challenges that
exist in lung cancer based on deep learning. The survey is systematically
written that combines regular mapping and literature review to review 32
conference and journal articles in the field from 2016 to 2021. After analysing
and reviewing the articles, the questions raised in the articles are being
answered. This research is superior to other review articles in this field due
to the complete review of relevant articles and systematic write up.
