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An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

DC FieldValueLanguage
dc.contributor.author조남훈-
dc.date.accessioned2021-01-19T08:15:15Z-
dc.date.available2021-01-19T08:15:15Z-
dc.date.issued2020-11-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181532-
dc.description.abstractProstate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist’s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the classification using pre-trained models and handcrafted features was carried out for comparative analysis. The binary classification was performed to classify between the two grade groups (benign vs. malignant) and quantile-quantile plots were used to show their predicted outcomes. Our proposed models for deep learning (DL) and machine learning (ML) classification achieved promising accuracies of 94.0% and 92.0%, respectively, based on non-handcrafted features extracted from CNN layers. Therefore, these models were able to predict nearly perfectly accurately using few trainable parameters or CNN layers, highlighting the importance of DL and ML techniques and suggesting that the computational analysis of microscopic anatomy will be essential to the future practice of pathology.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI AG-
dc.relation.isPartOfAPPLIED SCIENCES-BASEL-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleAn Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pathology (병리학교실)-
dc.contributor.googleauthorSubrata Bhattacharjee-
dc.contributor.googleauthorCho-Hee Kim-
dc.contributor.googleauthorDeekshitha Prakash-
dc.contributor.googleauthorHyeon-Gyun Park-
dc.contributor.googleauthorNam-Hoon Cho-
dc.contributor.googleauthorHeung-Kook Choi-
dc.identifier.doi10.3390/app10228013-
dc.contributor.localIdA03812-
dc.relation.journalcodeJ03706-
dc.identifier.eissn2076-3417-
dc.subject.keywordprostate carcinoma-
dc.subject.keywordmicroscopic-
dc.subject.keywordconvolutional neural network-
dc.subject.keywordmachine learning-
dc.subject.keyworddeep learning-
dc.subject.keywordhandcrafted-
dc.contributor.alternativeNameCho, Nam Hoon-
dc.contributor.affiliatedAuthor조남훈-
dc.citation.volume10-
dc.citation.number22-
dc.citation.startPage8013-
dc.identifier.bibliographicCitationAPPLIED SCIENCES-BASEL, Vol.10(22) : 8013, 2020-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers

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