ABSTRACT
The Internet of Health Things (IoHT) with a cloud computing (CC) environment provides diverse services to numerous people. The CC environment is applied for controlling a huge quantity of medical data. The process of detecting and classifying a brain tumor (BT) at the early stage prevents the severity or death rate caused. Hence, an entirely automated IoHT model with CC is required to be designed for identifying and classifying BTs. In this view, this chapter devises an effective IoHT with a CC-based BT identification model by the use of particle swarm optimization (PSO) with support vector machine (SVM). The proposed detection model comprises preprocessing, feature extraction, and classification. Once the inputted magnetic reasoning imaging (MRI) is preprocessed, the feature vectors will be extracted from the preprocessed image using the Gray-Level Co-occurrence Matrix (GLCM). Finally, the PSO-SVM classifier model is applied for the classification of BT images as benign or malign. The presented GLCM-PSO-SVM model is validated using a set of images from the brain tumor segmentation (BRATS) data set. The simulation outcome ensured that the PSO-SVM model is effective in terms of sensitivity, accuracy, and specificity.
