Innovations in Stroke Identification: A Machine Learning-Based Diagnostic Model Using Neuroimages

Abstract: Stroke diagnosis is a time-sensitive and critical healthcare process that requires rapid and accurate detection to ensure effective treatment and improved patient outcomes. Stroke is one of the leading causes of mortality, neurological disorders, and long-term disability worldwide, creating a significant burden on healthcare systems. Conventional diagnostic methods such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) depend heavily on expert radiological analysis, which may lead to delays during emergency situations where immediate medical intervention is essential. To overcome these limitations, this research presents a machine learning and deep learning- based stroke diagnosis system using neuroimaging data.The proposed framework utilizes advanced Deep Learning architectures, including Convolutional Neural Networks (CNN), Inception V3, and MobileNet, for efficient analysis and classification of brain neuroimages. The CNN model performs automated feature extraction from medical images, while Inception V3 enhances detection accuracy by capturing complex spatial and visual patterns through deep convolutional layers. Additionally, MobileNet provides a lightweight and computationally efficient architecture, enabling faster processing and suitability for real-time clinical applications. The integration of these models ensures both high diagnostic accuracy and reduced computational complexity.The system was trained and tested on neuroimaging datasets containing both stroke-affected and healthy brain scans. Experimental evaluation demonstrated superior performance in terms of accuracy, precision, sensitivity, and reliability for stroke prediction and classification. The proposed intelligent diagnostic model assists healthcare professionals in achieving faster stroke detection, early diagnosis, and timely treatment planning. By incorporating Artificial Intelligence (AI), Medical Image Processing, and Machine Learning techniques into the healthcare workflow, the system can significantly reduce diagnosis time, improve patient care quality, minimize human error, and decrease the economic burden associated with stroke management and rehabilitation.

Keywords: Stroke Diagnosis, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Inception V3, MobileNet, Neuroimaging, Medical Image Processing, Artificial Intelligence (AI), Brain Stroke Detection, CT Scan, MRI Scan, Healthcare Analytics, Stroke Prediction, Real-Time Diagnosis, Clinical Decision Support System.