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

dc.contributor.authorMohamed, A.A.A.
dc.contributor.authorHançerlioğullari, A.
dc.contributor.authorRahebi, J.
dc.contributor.authorRezaeizadeh, R.
dc.contributor.authorLopez-Guede, J.M.
dc.date.accessioned2024-07-22T09:08:08Z
dc.date.available2024-07-22T09:08:08Z
dc.date.issued2024
dc.description.abstractColon 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.
dc.identifier10.3390/diagnostics14131417
dc.identifier.doi10.3390/diagnostics14131417
dc.identifier.issn20754418
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85198356762
dc.identifier.urihttps://hdl.handle.net/20.500.12597/33429
dc.identifier.volume14
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofDiagnostics
dc.relation.ispartofseriesDiagnostics
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectColon cancer, convolutional neural network, Fishier Mantis Optimizer, FMO, metaheuristic methods
dc.titleColon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
dc.typearticle
dspace.entity.typeScopus
oaire.citation.issue13
oaire.citation.volume14
person.affiliation.nameKastamonu University
person.affiliation.nameKastamonu University
person.affiliation.nameIstanbul Topkapi University
person.affiliation.nameUniversity of Guilan
person.affiliation.nameUniversidad del Pais Vasco
person.identifier.orcid0000-0001-8344-6937
person.identifier.orcid0000-0001-9875-4860
person.identifier.orcid0000-0002-5310-1601
person.identifier.scopus-author-id57222441125
person.identifier.scopus-author-id18133515900
person.identifier.scopus-author-id36451137000
person.identifier.scopus-author-id59005277600
person.identifier.scopus-author-id34880300000

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