Revolutionizing Stroke Detection - Machine Learning Diagnostic Models Leveraging Neuroimages

Abstract

This Brain Stroke Detection System streamlines and improves stroke diagnosis by utilizing a three-layer Convolutional Neural Network (CNN) to process non-contrast CT scans. The system is developed with a Python backend using Flask, paired with a responsive frontend built on HTML, CSS, and JavaScript, providing an easy-to-use interface for clinical settings. Regarding Model Architecture & Performance, the custom CNN model exhibits strong diagnostic capability, achieving 98% accuracy on training data and 97% on validation, indicating high precision in detecting stroke-related anomalies. Comparable studies using ensemble CNN models—such as combinations of InceptionV3, Xception, and MobileNetV2—have shown similar effectiveness, reaching 98.9% accuracy, 98.5% recall, and an AUC of 98.7% on related CT datasets. Regarding Dataset & Generalization, training was conducted on a well-balanced dataset of 2,501 CT images, comprising 1,551 normal and 950 stroke-positive scans. This balanced distribution supports better generalization and reduces the risk of overfitting, adhering to best practices in AI for medical imaging. Regarding Clinical Relevance, by combining a high-performing CNN with a user-friendly web interface, this system presents a scalable and dependable tool for clinical stroke detection. Its accuracy is on par with leading deep learning solutions, underlining the growing role of AI in advancing medical imaging technologies.