Early brain stroke detection using multilayer perceptron of convolutional neural network-based residual network.

Background: Stroke, medically known as the brain attack, refers to the stoppage or stoppage of blood from flowing into a particular region of the brain, or even from the breaking of a vessel, causing injury to and death of areas of the brain. It presents a medical emergency, with the potential of severe long-term neurological impairment, disability, and even death; thus, urgent detection and treatment are needed.

Objective: The study aims to develop a novel Multilayer Perceptron of Convolutional Neural Network-based Residual Network (MLPCNNbRN) for early brain stroke detection, focusing on improving the accuracy and reliability of detecting subtle stroke patterns in medical images.

Methods: The MLPCNNbRN provided resented in the context of residual connections within an architecture designed for deep network training in medical images. This allowed the overall model to learn complex relations very effectively. The system was implemented in the Python framework. Its performance was compared with other methods. The key metrics used in the evaluation were accuracy, precision, recall, and F-score.

Results: The MLPCNNbRN model demonstrated superior performance compared to existing methods, achieving higher levels of accuracy in stroke detection. Specifically, the model improved overall accuracy, precision, recall, and F-score, showcasing its robustness in identifying subtle stroke patterns.

Conclusion: The proposed MLPCNNbRN system enhances early brain stroke detection by extracting hierarchical features and residual network learning, offering a more accurate and reliable approach than previous methods. This system has the potential to aid medical professionals in timely diagnosis and treatment, ultimately improving patient outcomes.