Deep Learning Applications for Lung Cancer Diagnosis: A systematic review

TL;DR

This systematic review examines the application of deep learning, particularly CNNs, in early lung cancer diagnosis, analyzing 32 studies from 2016 to 2021 to identify challenges and advancements.

Contribution

It provides a comprehensive systematic review of deep learning models for lung cancer diagnosis, highlighting accuracy, sensitivity, and existing challenges in the field.

Findings

Deep learning models show promising accuracy in early lung cancer detection.

Challenges include data scarcity and model interpretability.

The review offers insights for future research directions.

Abstract

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.