Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease

Journal of Digital Imaging

Volumn 31, Issue 4, 2018, Pages 415-424

  Kim, Guk Bae ^a   Jung, Kyu Hwan ^b   Lee, Yeha ^b   Kim, Hyun Jun ^b   Kim, Namkug ^c   Jun, Sanghoon ^c   Seo, Joon Beom ^c   Lynch, David A ^d  

^a Asan Medical Center   (South Korea)

^b Vuno   (South Korea)

^c University of Ulsan College of Medicine   (South Korea)

^d NATIONAL JEWISH MEDICAL AND RESEARCH CENTER   (United States)

Author keywords

Convolution neural network; Deep architecture; Interscanner variation; Interstitial lung disease; Support vector machine

Indexed keywords

BIOLOGICAL ORGANS; COMPUTERIZED TOMOGRAPHY; CONVOLUTION; OPACITY; PULMONARY DISEASES; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CONVOLUTION NEURAL NETWORK; DEEP ARCHITECTURES; HIGH-RESOLUTION COMPUTED TOMOGRAPHY; INTERSCANNER VARIATION; INTERSTITIAL LUNG DISEASE; MISCLASSIFICATION RATES; STRUCTURAL CHARACTERISTICS;

DEEP LEARNING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION; COHORT ANALYSIS; COMPARATIVE STUDY; DIAGNOSTIC IMAGING; FEMALE; HUMAN; INTERSTITIAL LUNG DISEASE; MALE; PATHOLOGY; PROCEDURES; RETROSPECTIVE STUDY; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; COHORT STUDIES; DEEP LEARNING; FEMALE; HUMANS; LUNG DISEASES, INTERSTITIAL; MALE; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RETROSPECTIVE STUDIES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85031507028     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-017-0028-9     Document Type: Article

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