Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
Yazarlar (5)
Amna Ali A. Mohamed
Kastamonu Üniversitesi, Türkiye
Kastamonu Üniversitesi, Türkiye
Prof. Dr. Aybaba HANÇERLİOĞULLARI Kastamonu Üniversitesi, Türkiye
Javad Rahebi
Istanbul Topkapi University, Türkiye
Istanbul Topkapi University, Türkiye
Rezvan Rezaeizadeh
University Of Guilan, İran
University Of Guilan, İran
Jose Manuel Lopez-Guede
Universidad Del Pais Vasco, İspanya
Universidad Del Pais Vasco, İspanya
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Diagnostics (Q1) | ||
| Dergi ISSN | 2075-4418 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2024 |
| Cilt / Sayı / Sayfa | 14 / 13 / – | DOI | 10.3390/diagnostics14131417 |
| Makale Linki | https://doi.org/10.3390/diagnostics14131417 | ||
| UAK Araştırma Alanları |
Nükleer Fizik
|
||
| Özet |
| 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 … |
| Anahtar Kelimeler |
| colon cancer | convolutional neural network | Fishier Mantis Optimizer | FMO | metaheuristic methods |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 18 |
| Google Scholar | 22 |