Real time brain stroke identification using face images based on machine learning and booby bird optimization

Stroke one of the most common causes of death after heart disease and cancer and is the leading cause of severe long-term disability. The earlier a stroke is detected and the faster it is treated, the greater the chance of recovery. Therefore, early detection and treatment of strokes are essential to save lives and reduce permanent damage. In this paper, we present a stroke monitoring strategy based on face images for individuals who have a risk of stroke or those with chronic diseases. Patients are monitored using a smart camera located in their room. This camera sends data to a fog server in the hospital, where all processes are done there. The proposed strategy consists of three main stages, which are i) data preprocessing, ii) model training, and iii) model testing. In the data preprocessing stage, faces are extracted from images using Yolo v8. Then features are extracted using the Active Appearance Model (AAM) model and dlib library. The proposed Binary Booby Bird Optimization (B³O) is used as a new feature selection method to select only relevant features from data sets. In the model training stage, each feature is divided into ranges and these ranges are divided into regions to minimize the data set size for fast detection. Finally, the model testing stage tests all proposed stages to detect stroke patients using the Navie Bayes (NB) classifier. The experiment results show that the proposed B³O and proposed stroke monitoring strategy achieve high accuracy of 94.18% and 98.43%, respectively.