Differentiating the Presence of Brain Stroke Types in MR Images using CNN Architecture

Stroke is reportedly the biggest cause of death and disability in the world, according to the World Health Organisation (WHO). The severity of a stroke can be lowered by recognising the many stroke warning indicators early on. Using CNN, the primary goal of this study is to predict the likelihood that a brain stroke would develop at an early stage.

The novelty of the proposed work is to acquire models that can accurately differentiate between stroke and no-stroke (normal) cases using MR Imaging sequences like DWI, SWI, GRE and T2 FLAIR aiding in timely diagnosis and treatment decisions.

A dataset comprising real time MRI scans of patients with stroke and no-stroke conditions was collected and preprocessed for model training. The preprocessing involves standardizing the resolution of the images, normalizing pixel values, and augmenting the dataset to enhance model generalization. The ResNet, DenseNet, EfficientNet, and VGG16 architectures were implemented and trained on the preprocessed dataset. The training process involved optimizing model parameters using stochastic gradient descent and minimizing the loss function.

The results demonstrate promising performance across all models by obtaining an accuracy of 98% for ResNet, DenseNet and EfficientNet, while 97% for VGG16 in differentiating the stroke using real time MRI data.

The proposed work explored the effectiveness of CNN models, including ResNet, DenseNet, EfficientNet, and VGG16, for the differentiation of stroke and no-stroke cases. The models were trained and evaluated using a real-time dataset of brain MR Images. The obtained accuracies highlight the potential of CNN models in accurately differentiating between stroke and non-stroke cases.