Deep ensemble learning-based classification of stroke

Stroke is one sort of brain disease that profoundly impairs people's quality of life and health. The quantitative analysis of brain Magnetic Resonance (MR) images is crucial for both the diagnosis and treatment of strokes. The method of early diagnosis is crucial for preventing stroke instances. Deep neural networks, which have the capacity for vast data learning, enable stroke prediction. Therefore, in this study, several deep neural network models are proposed for transfer learning to classify MRI images into two categories (stroke and non-stroke), in order to study the characteristics of the stroke lesions and achieve full intelligent automatic detection. These models include MobileNet, EfficientNetB2, ResNet50, DenseNet121, and EfficientNetB2. 1901 training images, 475 validation images and 250 testing images make up the study dataset. Data augmentation was employed to increase the number of images on the training and validation sets, which helped the models to learn more effectively. Results from the experiment outperform those from all state of the art methods that used the same dataset. The top models of the study, which use the DenseNet121 and Xception models for transfer learning, obtained an overall accuracy of 98.4% with the same values for precision, recall, and F1-score. Additionally, ensemble learning method is used with the top three models of the study, EfficientNet, DenseNet, and Xception, and a 100% overall score is obtained.