An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification
Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These...
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| Format: | Article |
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10634551/ |
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| author | Fahdah A Almarshad Prasanalakshmi Balaji Liyakathunisa Syed Eman Aljohani Santhi Muttipoll Dharmarajlu Thavavel Vaiyapuri Nourah Ali AlAseem |
| author_facet | Fahdah A Almarshad Prasanalakshmi Balaji Liyakathunisa Syed Eman Aljohani Santhi Muttipoll Dharmarajlu Thavavel Vaiyapuri Nourah Ali AlAseem |
| author_sort | Fahdah A Almarshad |
| collection | DOAJ |
| description | Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These models influence features extracted from medical imaging, genetic, and clinical data to distinguish between benign and malignant tumours, define cancer stages, and guide treatment verdicts. By automating cancer detection and classification procedures, DL techniques help healthcare experts make quicker and more exact analyses, leading to superior patient results, modified treatment tactics, and enhanced complete organization of GC cases. This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. The SOADL-GCC approach aims to examine the gastrointestinal tract images for the detection and classification of GC. To achieve it, the SOADL-GCC model employs a bilateral filtering (BF) approach for the noise removal process and enhances image quality. Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. Moreover, SOA can achieve the optimum assortment of hyperparameters associated with the CapsNet model. Finally, the classification process can be performed using the deep belief network (DBN) model. A sequence of simulations took place on the Kvasir dataset to evaluate improved detection results of SOADL-GCC technology. An extensive comparative study reported that the SOADL-GCC technique effectively performs well with other models with a maximum accuracy of 99.72%. |
| format | Article |
| id | doaj-art-418b152f1f114f0e81b0dbb0f8eeab5d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-418b152f1f114f0e81b0dbb0f8eeab5d2025-08-20T02:03:19ZengIEEEIEEE Access2169-35362024-01-011213723713724610.1109/ACCESS.2024.344283110634551An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer ClassificationFahdah A Almarshad0https://orcid.org/0009-0004-6047-7844Prasanalakshmi Balaji1https://orcid.org/0000-0002-6882-2233Liyakathunisa Syed2Eman Aljohani3https://orcid.org/0009-0001-5913-2978Santhi Muttipoll Dharmarajlu4https://orcid.org/0000-0003-0281-1731Thavavel Vaiyapuri5https://orcid.org/0000-0001-5494-5278Nourah Ali AlAseem6Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaCollege of Nursing, Jazan University, Jazan, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaGastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These models influence features extracted from medical imaging, genetic, and clinical data to distinguish between benign and malignant tumours, define cancer stages, and guide treatment verdicts. By automating cancer detection and classification procedures, DL techniques help healthcare experts make quicker and more exact analyses, leading to superior patient results, modified treatment tactics, and enhanced complete organization of GC cases. This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. The SOADL-GCC approach aims to examine the gastrointestinal tract images for the detection and classification of GC. To achieve it, the SOADL-GCC model employs a bilateral filtering (BF) approach for the noise removal process and enhances image quality. Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. Moreover, SOA can achieve the optimum assortment of hyperparameters associated with the CapsNet model. Finally, the classification process can be performed using the deep belief network (DBN) model. A sequence of simulations took place on the Kvasir dataset to evaluate improved detection results of SOADL-GCC technology. An extensive comparative study reported that the SOADL-GCC technique effectively performs well with other models with a maximum accuracy of 99.72%.https://ieeexplore.ieee.org/document/10634551/Gastric cancerhyperparameter tuningCapsNetcomputer-assisted diagnosisdeep learningfeature extraction |
| spellingShingle | Fahdah A Almarshad Prasanalakshmi Balaji Liyakathunisa Syed Eman Aljohani Santhi Muttipoll Dharmarajlu Thavavel Vaiyapuri Nourah Ali AlAseem An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification IEEE Access Gastric cancer hyperparameter tuning CapsNet computer-assisted diagnosis deep learning feature extraction |
| title | An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification |
| title_full | An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification |
| title_fullStr | An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification |
| title_full_unstemmed | An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification |
| title_short | An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification |
| title_sort | efficient optimal capsnet model based computer aided diagnosis for gastrointestinal cancer classification |
| topic | Gastric cancer hyperparameter tuning CapsNet computer-assisted diagnosis deep learning feature extraction |
| url | https://ieeexplore.ieee.org/document/10634551/ |
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