Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Poudel S. | - |
dc.contributor.author | Kim Y.J. | - |
dc.contributor.author | Vo D.M. | - |
dc.contributor.author | Lee S.-W. | - |
dc.date.available | 2020-07-13T00:35:28Z | - |
dc.date.created | 2020-06-19 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/64617 | - |
dc.description.abstract | Computer-aided diagnosis systems developed by computer vision researchers have helped doctors to recognize several endoscopic colorectal diseases more rapidly, which allows appropriate treatment and increases the patient's survival ratio. Herein, we present a robust architecture for endoscopic image classification using an efficient dilation in Convolutional Neural Network (CNNs). It has a high receptive field of view at the deep layers in increasing and decreasing dilation factor to preserve spatial details. We argue that dimensionality reduction in CNN can cause the loss of spatial information, resulting in miss of polyps and confusion in similar-looking images. Additionally, we use a regularization technique called DropBlock to reduce overfitting and deal with noise and artifacts. We compare and evaluate our method using various metrics: accuracy, recall, precision, and F1-score. Our experiments demonstrate that the proposed method provides the F1-score of 0.93 for Colorectal dataset and F1-score of 0.88 for KVASIR dataset. Experiments show higher accuracy of the proposed method over traditional methods when classifying endoscopic colon diseases. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000541127800025 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2996770 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.99227 - 99238 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85086314252 | - |
dc.citation.endPage | 99238 | - |
dc.citation.startPage | 99227 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Poudel S. | - |
dc.contributor.affiliatedAuthor | Kim Y.J. | - |
dc.contributor.affiliatedAuthor | Vo D.M. | - |
dc.contributor.affiliatedAuthor | Lee S.-W. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | colon disease classification | - |
dc.subject.keywordAuthor | colon disease classification with CNN | - |
dc.subject.keywordAuthor | Colorectal image classification | - |
dc.subject.keywordPlus | Computer aided diagnosis | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Dimensionality reduction | - |
dc.subject.keywordPlus | Endoscopy | - |
dc.subject.keywordPlus | Medical computing | - |
dc.subject.keywordPlus | Patient treatment | - |
dc.subject.keywordPlus | Colorectal Disease | - |
dc.subject.keywordPlus | Computer aided diagnosis systems | - |
dc.subject.keywordPlus | Dilation factor | - |
dc.subject.keywordPlus | Overfitting | - |
dc.subject.keywordPlus | Receptive fields | - |
dc.subject.keywordPlus | Regularization technique | - |
dc.subject.keywordPlus | Spatial informations | - |
dc.subject.keywordPlus | Survival ratio | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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