A Deep Ensemble Learning Method for Colorectal Polyp Classification With Optimized Network Parameters

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aut.relation.journalApplied Intelligenceen_NZ
dark.contributor.authorYounas, Fen_NZ
dark.contributor.authorUsman, Men_NZ
dark.contributor.authorYan, WQen_NZ
dc.date.accessioned2022-05-12T03:40:06Z
dc.date.available2022-05-12T03:40:06Z
dc.description.abstractColorectal Cancer (CRC), a leading cause of cancer-related deaths, can be abated by timely polypectomy. Computer-aided classification of polyps helps endoscopists to resect timely without submitting the sample for histology. Deep learning-based algorithms are promoted for computer-aided colorectal polyp classification. However, the existing methods do not accommodate any information on hyperparametric settings essential for model optimisation. Furthermore, unlike the polyp types, i.e., hyperplastic and adenomatous, the third type, serrated adenoma, is difficult to classify due to its hybrid nature. Moreover, automated assessment of polyps is a challenging task due to the similarities in their patterns; therefore, the strength of individual weak learners is combined to form a weighted ensemble model for an accurate classification model by establishing the optimised hyperparameters. In contrast to existing studies on binary classification, multiclass classification require evaluation through advanced measures. This study compared six existing Convolutional Neural Networks in addition to transfer learning and opted for optimum performing architecture only for ensemble models. The performance evaluation on UCI and PICCOLO dataset of the proposed method in terms of accuracy (96.3%, 81.2%), precision (95.5%, 82.4%), recall (97.2%, 81.1%), F1-score (96.3%, 81.3%) and model reliability using Cohen’s Kappa Coefficient (0.94, 0.62) shows the superiority over existing models. The outcomes of experiments by other studies on the same dataset yielded 82.5% accuracy with 72.7% recall by SVM and 85.9% accuracy with 87.6% recall by other deep learning methods. The proposed method demonstrates that a weighted ensemble of optimised networks along with data augmentation significantly boosts the performance of deep learning-based CAD.en_NZ
dc.identifier.citationApplied Intelligence (2022). https://doi.org/10.1007/s10489-022-03689-9
dc.identifier.doi10.1007/s10489-022-03689-9en_NZ
dc.identifier.issn0924-669Xen_NZ
dc.identifier.issn1573-7497en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15129
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s10489-022-03689-9
dc.rights(C) The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectColorectal Cancer; Deep learning; Ensemble learning; Prediction; Transfer learning; Virtual biopsy
dc.titleA Deep Ensemble Learning Method for Colorectal Polyp Classification With Optimized Network Parametersen_NZ
dc.typeJournal Article
pubs.elements-id454159
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Design and Creative Technologies
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