Open AccessAppraisal of deep-learning techniques on computer-aided lung cancer diagnosis with computed tomography screening
Author(s) -
S Akila Agnes,
J. Anitha
Publication year - 2020
Publication title -
journal of medical physics/journal of medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.292
H-Index - 24
eISSN - 1998-3913
pISSN - 0971-6203
DOI - 10.4103/jmp.jmp_101_19
Subject(s) - sørensen–dice coefficient , artificial intelligence , deep learning , cad , lung cancer , segmentation , convolutional neural network , lung cancer screening , false positive paradox , computer science , computer aided diagnosis , radiology , pattern recognition (psychology) , linear discriminant analysis , medicine , computed tomography , image segmentation , pathology , engineering drawing , engineering
Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications.