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Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification
Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advant...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262480/ https://www.ncbi.nlm.nih.gov/pubmed/35814569 http://dx.doi.org/10.1155/2022/6162445 |
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author | Al Duhayyim, Mesfer Mengash, Hanan Abdullah Marzouk, Radwa Nour, Mohamed K Mahgoub, Hany Althukair, Fahd Mohamed, Abdullah |
author_facet | Al Duhayyim, Mesfer Mengash, Hanan Abdullah Marzouk, Radwa Nour, Mohamed K Mahgoub, Hany Althukair, Fahd Mohamed, Abdullah |
author_sort | Al Duhayyim, Mesfer |
collection | PubMed |
description | Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent “semantic” feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network–long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches. |
format | Online Article Text |
id | pubmed-9262480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92624802022-07-08 Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification Al Duhayyim, Mesfer Mengash, Hanan Abdullah Marzouk, Radwa Nour, Mohamed K Mahgoub, Hany Althukair, Fahd Mohamed, Abdullah Comput Intell Neurosci Research Article Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent “semantic” feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network–long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches. Hindawi 2022-06-30 /pmc/articles/PMC9262480/ /pubmed/35814569 http://dx.doi.org/10.1155/2022/6162445 Text en Copyright © 2022 Mesfer Al Duhayyim et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Al Duhayyim, Mesfer Mengash, Hanan Abdullah Marzouk, Radwa Nour, Mohamed K Mahgoub, Hany Althukair, Fahd Mohamed, Abdullah Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title | Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title_full | Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title_fullStr | Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title_full_unstemmed | Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title_short | Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification |
title_sort | hybrid rider optimization with deep learning driven biomedical liver cancer detection and classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262480/ https://www.ncbi.nlm.nih.gov/pubmed/35814569 http://dx.doi.org/10.1155/2022/6162445 |
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