A Stroke Diagnosis System with CNN Algorithm Trained by Open-Access Brain CT Datasets

Stroke is a major cause of disability and death worldwide, with the incidence increasing with age. In China, millions of people are affected by stroke every year. The golden treatment time for stroke is only four hours; missed diagnosis or misdiagnosis can lead to permanent brain injury or death. CT imaging is an essential tool in stroke diagnosis, with different densities of brain CT images corresponding to different brain diseases. However, diagnosis relies on doctors’ experience and knowledge, leading to misdiagnosis and missed diagnoses. Computer-aided diagnosis (CAD) technology, including neural network technology in machine learning and open-access brain CT datasets, has shown promise in improving the accuracy of diagnosis and reducing missed diagnoses. This project aims to improve the efficiency and accuracy of CT image diagnosis in stroke by applying CAD technology to classify different densities of brain CT images, potentially improving patient outcomes and reducing the burden on clinical emergency doctors.