From Pixels to Predictions : Leveraging CNNs for Timely Ischemic Stroke Detection

Early detection of ischemic stroke is crucial for optimal patient outcomes. This research presents a Convolutional Neural Network (CNN) model developed using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Keras, and TensorFlow for the accurate identification of isYPchemic stroke. The model was trained and evaluated on a publicly available dataset of medical images. Through meticulous data preprocessing, augmentation, and model optimization, the CNN achieved a remarkable success rate of over 90% in distinguishing ischemic stroke cases from healthy controls. This study demonstrates the potential of deep learning in developing a robust and efficient clinical decision support tool for the timely diagnosis of ischemic stroke.