Systematic review for lung cancer detection and lung nodule classification: Taxonomy, challenges, and recommendation future works

• Published in: Journal of intelligent systems Vol. 31; no. 1; pp. 944 - 964
• Main Authors: Jassim, Mustafa Mohammed, Jaber, Mustafa Musa
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 10-08-2022; Walter de Gruyter GmbH
• Subjects: Algorithms; Artificial intelligence; Classification; convolution neural network; Deep learning; Diagnosis; Human error; Literature reviews; Lung cancer; lung carcinoma; lung nodule classification; Machine learning; Medical imaging; Medical research; Nodules; Scientific papers; Taxonomy
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2022-0062

Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of lung cancer classification of lung nodules in 4 reliable databases (Science Direct, Scopus, web of science, and IEEE), and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously.