Anas platyrhynchos optimizer with deep transfer learning-based gastric cancer classification on endoscopic images

Mashael S. Maashi; Yasser Ali Reyad Ali; Abdelwahed Motwakel; Amira Sayed A. Aziz; Manar Ahmed Hamza; Amgad Atta Abdelmageed; Mashael S. Maashi; Yasser Ali Reyad Ali; Abdelwahed Motwakel; Amira Sayed A. Aziz; Manar Ahmed Hamza; Amgad Atta Abdelmageed

doi:10.3934/era.2023162

Electronic Research Archive

2023, Volume 31, Issue 6: 3200-3217. doi: 10.3934/era.2023162

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Anas platyrhynchos optimizer with deep transfer learning-based gastric cancer classification on endoscopic images

1.
Department of Software Engineering, College of Computer and Information Science, King Saud University, PO Box 103786, Riyadh 11543, Saudi Arabia
2.
Department of Information System, College of Computer and Information Science, King Saud University, PO Box 103786, Riyadh 11543, Saudi Arabia
3.
Department of Information Systems, College of business administration in Hawtat bani Tamim, Prince Sattam bin Abdulaziz University, Saudi Arabia
4.
Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
5.
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Academic Editor: William Guo

Received: 02 January 2023 Revised: 19 January 2023 Accepted: 01 February 2023 Published: 27 March 2023

Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose the GC at initial stages itself to save the lives. Histopathological analysis remains the gold standard for accurate diagnosis of the disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years for the diagnosis of diseases, it is challenging to apply in this case, due to the lack of accessible gastric histopathological image databases. With a rapid progression in the Computer Vision (CV) technologies, particularly, the emergence of medicinal image classifiers, it has become feasible to examine all the types of electron micrographs in a rapid and an effective manner. Therefore, the current research article presents an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for the classification of GC using the endoscopic images. The aim of the proposed APODL-GCC method is to identify the presence of GC with the help of CV and Deep Learning concepts. Primarily, the APODL-GCC technique employs a contrast enhancement technique. Next, the feature extraction process is performed using a neural architectural search network model to generate a collection of feature vectors. For hyperparameter optimization, the Anas Platyrhynchos Optimizer (APO) algorithm is used which enhances the classification performance. Finally, the GC classification process is performed using the Deep Belief Network method. The proposed APODL-GCC technique was simulated using medical images and the experimental results established that the APODL-GCC technique accomplishes enhanced performance over other models.
- gastric cancer,
- artificial intelligence,
- deep learning,
- computer vision,
- NASNet model,
- endoscopic images
Citation: Mashael S. Maashi, Yasser Ali Reyad Ali, Abdelwahed Motwakel, Amira Sayed A. Aziz, Manar Ahmed Hamza, Amgad Atta Abdelmageed. Anas platyrhynchos optimizer with deep transfer learning-based gastric cancer classification on endoscopic images[J]. Electronic Research Archive, 2023, 31(6): 3200-3217. doi: 10.3934/era.2023162

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Abstract

Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose the GC at initial stages itself to save the lives. Histopathological analysis remains the gold standard for accurate diagnosis of the disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years for the diagnosis of diseases, it is challenging to apply in this case, due to the lack of accessible gastric histopathological image databases. With a rapid progression in the Computer Vision (CV) technologies, particularly, the emergence of medicinal image classifiers, it has become feasible to examine all the types of electron micrographs in a rapid and an effective manner. Therefore, the current research article presents an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for the classification of GC using the endoscopic images. The aim of the proposed APODL-GCC method is to identify the presence of GC with the help of CV and Deep Learning concepts. Primarily, the APODL-GCC technique employs a contrast enhancement technique. Next, the feature extraction process is performed using a neural architectural search network model to generate a collection of feature vectors. For hyperparameter optimization, the Anas Platyrhynchos Optimizer (APO) algorithm is used which enhances the classification performance. Finally, the GC classification process is performed using the Deep Belief Network method. The proposed APODL-GCC technique was simulated using medical images and the experimental results established that the APODL-GCC technique accomplishes enhanced performance over other models.

References

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