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

The early and accurate diagnosis of stroke is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods often face challenges in achieving high accuracy and efficiency. In this study, we propose an innovative machine learning-based diagnostic model utilizing ResNet and MobileNet architectures to classify neuroimages into normal and stroke categories. Our approach leverages the robust feature extraction capabilities of ResNet and the lightweight, efficient nature of MobileNet to create a comprehensive diagnostic tool. The model is trained on a diverse dataset of neuroimages, incorporating advanced preprocessing techniques to enhance its generalizability and performance. Initial experiments demonstrate that ResNet achieves a training accuracy of 94% with normal images, while MobileNet achieves an impressive 92% training accuracy with normal images. These results highlight the potential of our proposed model to significantly improve the accuracy and speed of stroke diagnosis, providing a valuable tool for clinicians and healthcare providers. Future work will focus on further validation with larger datasets and real-world clinical trials to establish the model's efficacy and reliability in clinical settings. This study underscores the transformative potential of deep learning models in advancing stroke diagnosis and enhancing patient care.

Keywords: Stroke Diagnosis, Machine Learning, Deep Learning, ResNet, MobileNet, Neuroimages, Medical Imaging, Stroke Classification, Diagnostic Model, Healthcare AI.