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Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

Computers, Materials & Continua 2023年第4期75卷 785-798页

作者： Kamalrulnizam Bin Abu Bakar Fatima Tul Zuhra Babangida Isyaku Fuad A.Ghaleb Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Science and Technology Shaheed Benazir Bhutto University Sanghar CampusPakistan Faculty of Computing and Information Technology Sule Lamido UniversityKafin HausaP.M.B.048Nigeria

The Internet of Medical Things (IoMT) emerges with the visionof the wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

关键词： Artificial intelligence internet of things internet of medical things wireless body sensor network wireless endoscopic capsule gastric cancer

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