Neuroimage-Based Stroke Identification: A Machine Learning Approach

Abstract

Stroke diagnosis is a time-sensitive process that demands rapid and accurate identification to ensure timely treatment. This study introduces a machine learning-based diagnostic model for stroke detection using neuroimaging data. Stroke remains a major global health concern, contributing to high mortality and disability rates. However, conventional diagnostic techniques, such as CT and MRI scans, often require expert analysis, leading to delays that can reduce treatment effectiveness, especially in acute cases where every minute matters.To address this challenge, our research leverages deep learning to automate stroke detection from neuroimages. We utilized Convolutional Neural Networks (CNN) with InceptionV3 and MobileNet architectures to process brain scans and predict stroke occurrences. InceptionV3, known for its deep convolutional layers that capture intricate image features, and MobileNet, optimized for efficiency and speed, enable both detailed analysis and fast processing. The model was trained on extensive neuroimaging datasets to differentiate between healthy brain tissues and stroke-affected regions. Our results demonstrate high accuracy in stroke identification, highlighting the model’s potential to assist healthcare professionals in faster and more precise diagnoses. Integrating this machine learning model into existing diagnostic workflows could significantly reduce the time to diagnosis, allowing for earlier treatment and improved patient outcomes. By combining speed, accuracy, and automation, this approach has the potential to enhance stroke care and alleviate the economic burden associated with delayed treatment.