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Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection

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Abstract

Gastrointestinal (GI) cancer detection includes the detection of cancerous or potentially cancerous lesions within the GI tract. Earlier diagnosis is critical for increasing the success of treatment and improving patient outcomes. Medical imaging plays a major role in diagnosing and detecting GI cancer. CT scans, endoscopy, MRI, ultrasound, and positron emission tomography (PET) scans can help detect lesions, abnormal masses, and changes in tissue structure. Artificial intelligence (AI) and machine learning (ML) methods are being gradually applied to medical imaging for cancer diagnosis. ML algorithms, including deep learning methodologies like convolutional neural network (CNN), are applied frequently for cancer diagnosis. These models learn features and patterns from labelled datasets to discriminate between normal and abnormal areas in medical images. This article presents a new Harbor Seal Whiskers Optimization Algorithm with Deep Learning based Medical Imaging Analysis for Gastrointestinal Cancer Detection (HSWOA-DLGCD) technique. The goal of the HSWOA-DLGCD algorithm is to explore the GI images for the cancer diagnosis. In order to accomplish this, the HSWOA-DLGCD system applies bilateral filtering (BF) approach for the removal of noise. In addition, the HSWOA-DLGCD technique makes use of HSWOA with Xception model for feature extraction. For cancer recognition, the HSWOA-DLGCD technique applies extreme gradient boosting (XGBoost) model. Finally, the parameters compared with the XGBoost system can be selected by moth flame optimization (MFO) system. The experimental results of the HSWOA-DLGCD technique could be verified on the Kvasir database. The simulation outcome demonstrated a best possible solution of the HSWOA-DLGCD method than other recent methods.

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Data availability

The data that support the findings of this study are available in the Kvasir database, reference number [24].

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Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/117/44. Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R507), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2024- 1576-01".

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Authors and Affiliations

  1. Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Amal Alshardan & Radwa Marzouk

  2. Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia

    Muhammad Kashif Saeed

  3. Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

    Shoayee Dlaim Alotaibi

  4. Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, 91911, Rafha, Saudi Arabia

    Abdullah M. Alashjaee

  5. Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    Nahla Salih

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  1. Amal Alshardan
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  2. Muhammad Kashif Saeed
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  3. Shoayee Dlaim Alotaibi
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  4. Abdullah M. Alashjaee
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  5. Nahla Salih
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Correspondence to Abdullah M. Alashjaee.

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Alshardan, A., Saeed, M.K., Alotaibi, S.D. et al. Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection. Health Inf Sci Syst 12, 35 (2024). https://doi.org/10.1007/s13755-024-00294-7

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