New bidirectional recurrent neural network optimized by improved Ebola search optimization algorithm for lung cancer diagnosis
•An optimal neural network for face detection based on metaheuristic technique.•Bidirectional recurrent neural network (BRNN) is used for the purpose.•The network has been optimized by a novel improved version of Ebola optimization algorithm.•The proposed procedure has been examined on GTFD (Georgia...
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| Published in | Biomedical signal processing and control Vol. 84; p. 104965 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.07.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1746-8094 1746-8108 |
| DOI | 10.1016/j.bspc.2023.104965 |
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| Abstract | •An optimal neural network for face detection based on metaheuristic technique.•Bidirectional recurrent neural network (BRNN) is used for the purpose.•The network has been optimized by a novel improved version of Ebola optimization algorithm.•The proposed procedure has been examined on GTFD (Georgia Tech Face Database).•The results compared with some diverse state of the art published methods.
The early detection of cancerous and malignant lung cancer by medical imaging techniques, CT-scan for example, which never needs to do sampling reduces the risk of cancer growth and spreading. Accordingly, computer image processing and diagnostic system development, followed by cancer's classification into malignant and benign, is of primary importance in the early discovery of lung cancer which plays a pivotal role in the treatment improvement and saving the patient's life. This work intended to improve malignant and benign gland categorization accuracy and, as a result, detection accuracy. Here, a new methodology has been proposed to get an accurate lung cancer diagnosis system using an improved Bidirectional Recurrent neural network. The improvement of the network has been done by designing an improved form of an Ebola optimization search algorithm. Before applying the major diagnosis system, some preprocessing techniques have been done. The model is then applied to IQ-OTH/NCCD lung cancer dataset and its results are compared with some published works to indicate the eminence of the suggested method toward the comparative ones. |
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| AbstractList | •An optimal neural network for face detection based on metaheuristic technique.•Bidirectional recurrent neural network (BRNN) is used for the purpose.•The network has been optimized by a novel improved version of Ebola optimization algorithm.•The proposed procedure has been examined on GTFD (Georgia Tech Face Database).•The results compared with some diverse state of the art published methods.
The early detection of cancerous and malignant lung cancer by medical imaging techniques, CT-scan for example, which never needs to do sampling reduces the risk of cancer growth and spreading. Accordingly, computer image processing and diagnostic system development, followed by cancer's classification into malignant and benign, is of primary importance in the early discovery of lung cancer which plays a pivotal role in the treatment improvement and saving the patient's life. This work intended to improve malignant and benign gland categorization accuracy and, as a result, detection accuracy. Here, a new methodology has been proposed to get an accurate lung cancer diagnosis system using an improved Bidirectional Recurrent neural network. The improvement of the network has been done by designing an improved form of an Ebola optimization search algorithm. Before applying the major diagnosis system, some preprocessing techniques have been done. The model is then applied to IQ-OTH/NCCD lung cancer dataset and its results are compared with some published works to indicate the eminence of the suggested method toward the comparative ones. |
| ArticleNumber | 104965 |
| Author | Tajally, AmirReza Bokov, Dmitry Ali Alkhazaleh, Hamzah Sabzalian, Mohammad Hosein Kharajinezhadian, Farzam Reihanisaransari, Reza |
| Author_xml | – sequence: 1 givenname: Mohammad Hosein surname: Sabzalian fullname: Sabzalian, Mohammad Hosein organization: Department of Mechanical Engineering, University of Santiago of Chile (USACH), Avenida Libertador Bernardo O’Higgins 3363, Santiago 9170022, Chile – sequence: 2 givenname: Farzam surname: Kharajinezhadian fullname: Kharajinezhadian, Farzam organization: Faculty of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran – sequence: 3 givenname: AmirReza surname: Tajally fullname: Tajally, AmirReza organization: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran – sequence: 4 givenname: Reza orcidid: 0000-0002-5177-0633 surname: Reihanisaransari fullname: Reihanisaransari, Reza organization: Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA – sequence: 5 givenname: Hamzah surname: Ali Alkhazaleh fullname: Ali Alkhazaleh, Hamzah organization: College of Engineering and IT, University of Dubai, Academic City, 14143 Dubai, United Arab Emirates – sequence: 6 givenname: Dmitry surname: Bokov fullname: Bokov, Dmitry email: bokovdmitry64@gmail.com organization: Institute of Pharmacy, Sechenov First Moscow State Medical University, 8 Trubetskaya St., bldg. 2, Moscow 119991, Russian Federation |
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