Open AccessA Systematic Review and Analysis on Deep Learning Techniques Used in Diagnosis of Various Categories of Lung Diseases
Author(s) -
Sreedevi Jasthy,
Vangipuram Radhakrishna,
Sushama Rani Dutta
Publication year - 2021
Publication title -
mendel ... (brno. on-line)/mendel ...
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.221
H-Index - 13
eISSN - 1803-3822
pISSN - 1803-3814
DOI - 10.13164/mendel.2021.2.080
Subject(s) - medicine , medical physics , scopus , radiology , systematic review , lung disease , lung ultrasound , disease , lung , web of science , lung cancer , computed tomography , pathology , medline , meta analysis , ultrasound , political science , law
One of the record killers in the world is lung disease. Lung disease denotes to many disorders affecting the lungs. These diseases can be identified through Chest X- Ray, Computed Tomography CT, Ultrasound tests. This study provides a systematic review on different types of Deep Learning (DL) designs, methods, techniques used by different researchers in diagnosing COVID-19, Pneumonia, Tuberculosis, Lung tumor, etc. In the present research study, a systematic review and analysis is carried by following PRISMA research methodology. For this study, more than 900 research articles are considered from various indexing sources such as Scopus and Web of Science. After several selection steps, finally a 40 quality research articles are included for detailed analysis. From this study, it is observed that majority of the research articles focused on DL techniques with Chest X-Ray images and few articles focused on CT scan images and very few have focused on Ultrasound images to identify the lung disease