부산대학교 도서관

In recent years, Internet of Things enabling applications, which have provided excellent answers to a variety of challenges. This fast-growing industry is led by wireless sensor networks. Smart medical devices and wearables, for example, play an important part in the Internet of Things, as they may collect a variety of longitudinal patient-generated health data while also presenting preliminary diagnosis options. As part of their efforts to serve patients with IoT-based solutions, experts apply ml to give effective resolutions in bleeding detection. This work describes a smart IoT-based solution for human brain hemorrhage diagnostics that uses deep learning algorithms to reduce death rates and provide correct treatment recommendations. The SVM and Recurrent Neural Network were used to classify the images from the computed tomography scans for the intracranial dataset. When compared to prior techniques such as naive bayes, KNN, and K-medoids, the classification results for the SVM and Recurrent neural network are high. According to the findings, the recurrent neural network beats other methods for identifying intracranial images. The output of the classification tool offers information on the type of brain hemorrhage, which helps to validate an expert’s diagnosis and is utilized as a learning tool for trainee radiologists to eliminate errors in existing systems.