A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahmad, Shahab | - |
dc.contributor.author | Ullah, Tahir | - |
dc.contributor.author | Ahmad, Ijaz | - |
dc.contributor.author | AL-Sharabi, Abdulkarem | - |
dc.contributor.author | Ullah, Kalim | - |
dc.contributor.author | Khan, Rehan Ali | - |
dc.contributor.author | Rasheed, Saim | - |
dc.contributor.author | Ullah, Inam | - |
dc.contributor.author | Uddin, Md. Nasir | - |
dc.contributor.author | Ali, Md. Sadek | - |
dc.date.accessioned | 2023-07-12T01:41:54Z | - |
dc.date.available | 2023-07-12T01:41:54Z | - |
dc.date.created | 2023-07-12 | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.issn | 1687-5265 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88481 | - |
dc.description.abstract | Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.relation.isPartOf | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | - |
dc.title | A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000834156700008 | - |
dc.identifier.doi | 10.1155/2022/8141530 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, v.2022 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85133269150 | - |
dc.citation.title | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | - |
dc.citation.volume | 2022 | - |
dc.contributor.affiliatedAuthor | Ullah, Inam | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | BREAST | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.