Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to th...

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Detalhes bibliográficos
Autores: Mohamed, Amna Ali A., Hançerlioğullari, Aybaba, Rahebi, Javad, Rezaeizadeh, Rezvan, López Guede, José Manuel
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/69112
Acesso em linha:http://hdl.handle.net/10810/69112
Access Level:acceso abierto
Palavra-chave:convolutional neural network
metaheuristic methods
FMO
Fishier Mantis Optimizer
colon cancer
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spelling Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis OptimizerMohamed, Amna Ali A.Hançerlioğullari, AybabaRahebi, JavadRezaeizadeh, RezvanLópez Guede, José Manuelconvolutional neural networkmetaheuristic methodsFMOFishier Mantis Optimizercolon cancerColon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.MDPI2024202420242024info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/69112reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2075-4418/14/13/1417info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/es/© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).oai:addi.ehu.eus:10810/691122026-06-18T09:23:17Z
dc.title.none.fl_str_mv Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
title Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
spellingShingle Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
Mohamed, Amna Ali A.
convolutional neural network
metaheuristic methods
FMO
Fishier Mantis Optimizer
colon cancer
title_short Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
title_full Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
title_fullStr Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
title_full_unstemmed Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
title_sort Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
dc.creator.none.fl_str_mv Mohamed, Amna Ali A.
Hançerlioğullari, Aybaba
Rahebi, Javad
Rezaeizadeh, Rezvan
López Guede, José Manuel
author Mohamed, Amna Ali A.
author_facet Mohamed, Amna Ali A.
Hançerlioğullari, Aybaba
Rahebi, Javad
Rezaeizadeh, Rezvan
López Guede, José Manuel
author_role author
author2 Hançerlioğullari, Aybaba
Rahebi, Javad
Rezaeizadeh, Rezvan
López Guede, José Manuel
author2_role author
author
author
author
dc.subject.none.fl_str_mv convolutional neural network
metaheuristic methods
FMO
Fishier Mantis Optimizer
colon cancer
topic convolutional neural network
metaheuristic methods
FMO
Fishier Mantis Optimizer
colon cancer
description Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/69112
url http://hdl.handle.net/10810/69112
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2075-4418/14/13/1417
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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