Recent advancements in deep learning based lung cancer detection: A systematic review

Abstract

Cancer is considered to be a key cause of substantial fatality and morbidity in the world. A report from the International Agency for Research on Cancer (IARC) states that 27 million new cases of cancer are expected before 2030. 1 in 18 men and 1 in 46 women are estimated to develop lung cancer over a lifetime. This paper discusses an overview of lung cancer, along with publicly available benchmark data sets for research purposes. Recent research performed in medical image analysis of lung cancer using deep learning algorithms is compared using various technical aspects such as efficiency, advantages, and limitations. These discussed approaches provide insight into techniques that can be used to perform the detection and classification of lung cancer. Numerous techniques adapted in the acquisition of the images, extraction of relevant features, segmentation of region affected, selection of optimal features, and classification are also discussed. The paper is concluded by stating the clinical, technical challenges and prominent future directions.