Lung nodule classification using artificial crawlers, directional texture and support vector machine

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Lung nodule classification using artificial crawlers, directional texture and support vector machine

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作者：Bruno Rodrigues Froz ; ^a ; ^{bfroz63@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Antonio Oseas de Carvalho Filho^a ; ^{antoniooseas@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Aristó ; fanes Corrê ; a Silva^a ; ^{ari@dee.ufma.br" class="auth_mail" title="E-mail the corresponding author} ; Anselmo Cardoso de Paiva^a ; ^{paiva@deinf.ufma.br" class="auth_mail" title="E-mail the corresponding author} ; Rodolfo Acatauassú ; Nunes^b ; ^{rodolfoacatauassu@yahoo.com.br" class="auth_mail" title="E-mail the corresponding author} ; Marcelo Gattass^c ; ^{mgattass@tecgraf.puc-rio.br" class="auth_mail" title="E-mail the corresponding author}
关键词：Artificial life ; Artificial crawlers ; Rose diagram ; Lung nodule classification
刊名：Expert Systems with Applications
出版年：2017
出版时间：1 March 2017
年：2017
卷：69
期：Complete
页码：176-188
全文大小：1872 K

文摘

class="label">•: We develop a methodology to classify lung nodule and non-nodules.
class="label">•: 833 lung scans were extracted from LIDC-IRDI database.
class="label">•: Three methods used was artificial crawlers, rose diagram and hybrid model.
class="label">•: The artificial crawlers was adapted to work on 3D images.
class="label">•: The best result was achieved from hybrid model, with accuracy of 94,3%.

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