Innovations in Stroke Evaluation: A Diagnostic System implementing Neuroimage-Based Machine Learning

Even as diseases of the circulation in the brain particularly-strokes rank amongst leading causes of death and disability in the world they are not completely untreatable maladies. The early detection and management of cases of the disease are critical for reducing the effects of stroke and optimizing the patients’ experience. Recent focus has been made on the application of the machine learning approaches for the stroke identification process. Its objective comprises such goals as determining helpful approaches, methodologies, and characteristics that help medical practitioners make proper decisions about the treatment and prevention of stroke. In this way, by our initiative, the new, efficient system for early stroke detection using primary brain CT-scan has been developed. This system integrates genetic algorithm made use of in selecting the ideal features with a bidirectional long short-term memory (BiLSTM) network to locate and confirm diseases in their preliminary stages- Strokes here. This system combines genetic algorithm and neural networks to identify the features that are important for classification and pass them to the BiLSTM model. In addition, we could perform the comparative analyses with the "matched" techniques of the first kind, which is based on the systematic comparison with the tests used in the classical tradition of mathematical statistics.