A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

Abstract

Stroke ranks as one of the deadliest diseases globally, emphasizing the crucial need for early diagnosis. This study aims to create a two-stage classification system for stroke and non-stroke images to support early clinical detection. Deep learning, a cornerstone of diagnosis, detection, and prompt treatment, is the primary methodology. Transfer learning adapts successful deep learning architectures for diverse problems, and ensemble learning combines multiple classifiers for enhanced results. These two techniques are applied to classify stroke using a dataset of stroke and normal images. In the initial stage, six pre-trained models are fine-tuned, with DenseNet, Xception, and EfficientNetB2 emerging as the top performers, achieving validation accuracies of 98.4%, 98.4%, and 98%, respectively. These models serve as base learners within an ensemble framework. A weighted average ensemble method combines them, resulting in a remarkable 99.84% accuracy on a reserved test dataset. This approach exhibits promise for stroke detection, a life-threatening condition, while also demonstrating the effectiveness of ensemble techniques in enhancing model performance.