Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Abstract: Cancer is the leading cause of deaths across the globe and 10 million people died of cancer and particularly 2.21 million new cases registered besides 1.80 million deaths, according to WHO, in 2020. Malignant cancer is caused by multiplication and growth of lung cells. In this context, exploiting technological innovations for automatic detection of lung cancer early is to be given paramount importance. Towards this end significant progress has been made and deep learning model such as Convolutional Neural Network (CNN) is found superior in processing lung CT or MRI images for disease diagnosis. Lung cancer detection in the early stages of the disease helps in better treatment and cure of the disease. In this paper, we made a systematic review of deep learning methods for detection of lung cancer. It reviews peer reviewed journal papers and conferences from 2012 to 2021. Literature review throws light on synthesis of different existing methods covering machine learning (ML), deep learning and artificial intelligence (AI). It provides insights of different deep learning methods in terms of their pros and cons and arrives at possible research gaps. This paper gives knowledge to the reader on different aspects of lung cancer detection which can trigger further research possibilities to realize models that can be used in Clinical Decision Support Systems (CDSSs) required by healthcare units.

Mattakoyya Aharonu and R Lokesh Kumar. “Systematic Review of Deep Learning Techniques for Lung Cancer Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.3 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140384

@article{Aharonu2023,
title = {Systematic Review of Deep Learning Techniques for Lung Cancer Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140384},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140384},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Mattakoyya Aharonu and R Lokesh Kumar}
}