ABSTRACT
The Internet of Healthcare Things (IoHT), a branch of the Internet of Things (IoT), is expected to bring promising benefits to all involved stakeholders and accelerate the revolution of the healthcare sector through a transition toward preventive and personalized medicine. Wireless Capsule Endoscopy (WCE) is applied to capture the region of the human gastrointestinal (GI) tract, which has been found to be very difficult by traditional endoscopic models. To explore the different patterns existing in the WCE images to find the bleeding regions, this chapter presents a new Improved Grey Wolf Optimization (IGWO)–based support vector machine (SVM) model. The proposed method contains a group of different processes namely data collection, preprocessing, feature extraction, and classification. Once the data is collected and preprocessed, a proficient normalized gray-level co-occurrence matrix (NGLCM) technique is utilized to extract the features from the provided GI images. Then, the classification process is carried out by the use of IGWO-SVM, where the parameters of SVM have been tuned by the IGWO algorithm. The simulation of the NGLCM-IGWO-SVM model takes place using benchmark GI images. The experimental outcome pointed out that the NGLCM-IGWO-SVM model is superior to other models with a maximum accuracy of 92.76%, a specificity of 94.87%, and a sensitivity of 90.35%.
