Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 15;12(1):35.
doi: 10.1007/s13755-024-00294-7. eCollection 2024 Dec.

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

Amal Alshardan  1 , Muhammad Kashif Saeed  2 , Shoayee Dlaim Alotaibi  3 , Abdullah M Alashjaee  4 , Nahla Salih  5 , Radwa Marzouk  1
Affiliations

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

Amal Alshardan et al. Health Inf Sci Syst. .

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.

Keywords: Artificial intelligence; Bio-inspired algorithm; Computer-aided diagnosis; Gastrointestinal cancer; Medical image analysis.

PubMed Disclaimer

Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Figures

Fig. 1
Fig. 1
Workflow of HSWOA-DLGCD technique
Fig. 2
Fig. 2
Structure of XGBoost
Fig. 3
Fig. 3
Sample images
Fig. 4
Fig. 4
Confusion matrices a, b 80:20 of TRAPH/TESPH and c, d 70:30 of TRAPH/TESPH
Fig. 5
Fig. 5
Average of HSWOA-DLGCD technique at 80:20 of TRAPH/TESPH
Fig. 6
Fig. 6
Average of HSWOA-DLGCD model at 70:30 of TRAPH/TESPH
Fig. 7
Fig. 7
Accuy curve of HSWOA-DLGCD technique at 80:20 of TRAPH/TESPH
Fig. 8
Fig. 8
Loss curve of HSWOA-DLGCD technique at 80:20 of TRAPH/TESPH
Fig. 9
Fig. 9
PR curve of HSWOA-DLGCD technique at 80:20 of TRAPH/TESPH
Fig. 10
Fig. 10
ROC curve of HSWOA-DLGCD technique at 80:20 of TRAPH/TESPH
Fig. 11
Fig. 11
Accuy and AUC outcome of HSWOA-DLGCD algorithm with recent methods
Fig. 12
Fig. 12
Sensy and specy outcome of HSWOA-DLGCD algorithm with recent methods
See this image and copyright information in PMC

References

    1. Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. In: Seminars in cancer biology. Academic Press; 2023. - PubMed
    1. Sharanyaa S, Vijayalakshmi S, Therasa M, Kumaran U, Deepika R. DCNET: a novel implementation of gastric cancer detection system through deep learning convolution networks. In 2022 international conference on advanced computing technologies and applications (ICACTA) (pp. 1–5). IEEE; 2022.
    1. Su X, Liu Q, Gao X, Ma L. Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images. Technology and Health Care, (Preprint), 2023:1–10. - PMC - PubMed
    1. Li C, Qin Y, Zhang WH, Jiang H, Song B, Bashir MR, Xu H, Duan T, Fang M, Zhong L, Meng L. Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. Med Phys. 2022;49(3):1535–46. - DOI - PubMed
    1. Guo Z, Lan J, Wang J, Hu Z, Wu Z, Quan J, Han Z, Wang T, Du M, Gao Q, Xue Y. Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning. Biomed Signal Process Control. 2023;86:105336. - DOI

LinkOut - more resources