Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Yang, Eunmok | - |
dc.contributor.author | Shankar, K. | - |
dc.contributor.author | Kumar, Sachin | - |
dc.contributor.author | Seo, Changho | - |
dc.contributor.author | Moon, Inkyu | - |
dc.date.accessioned | 2024-01-23T16:10:21Z | - |
dc.date.available | 2024-01-23T16:10:21Z | - |
dc.date.created | 2024-01-11 | - |
dc.date.issued | 2023-12 | - |
dc.identifier.issn | 2227-9059 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/47651 | - |
dc.description.abstract | The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics. © 2023 by the authors. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/biomedicines11123200 | - |
dc.identifier.scopusid | 2-s2.0-85180657484 | - |
dc.identifier.bibliographicCitation | Biomedicines, v.11, no.12 | - |
dc.description.isOpenAccess | TRUE | - |
dc.subject.keywordAuthor | cancer diagnosis | - |
dc.subject.keywordAuthor | prostate cancer | - |
dc.subject.keywordAuthor | magnetic resonance imaging | - |
dc.subject.keywordAuthor | equilibrium optimizer | - |
dc.subject.keywordAuthor | deep learning | - |
dc.citation.number | 12 | - |
dc.citation.title | Biomedicines | - |
dc.citation.volume | 11 | - |