EfficientNetB0 for Brain Stroke Classification on Computed Tomography Scan

The EfficientNetB0 model is used in this research work to propose an innovative method for the categorization of brain strokes based on computed tomography (CT) data. The categorization of strokes is an essential step in the medical diagnostic process since timely identification and treatment have been shown to dramatically improve patient outcomes. However, conventional approaches of classifying strokes based on CT scans can take extensive amounts of time as well as specialized expertise. This research study proposes a novel method for the classification of stroke cases that is both automated and efficient by making use of the capabilities of deep learning. EfficientNetB0 model on a large dataset of CT scans, and as a result, a classification accuracy of 97% has been achieved. The proposed methodology performed far better than other approaches now considered to be state-of-the-art, and it has the potential to assist doctors in making diagnoses that are both more accurate and more time efficient.