Brain Stroke Prediction Using Deep Learning: A CNN Approach

Madhavi K. Reddy, Karthik Kovuri, J. Avanija, M. Sakthivel, Shivaprasad Kaleru

20222022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)22 citationsDOI

Abstract

A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. It's a medical emergency; therefore getting help as soon as possible is critical. Seeking medical help right away can help prevent brain damage and other complications. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome, and supporting doctors in prescribing disease treatment. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. In addition, three models for predicting the outcomes have been developed. This suggested study uses a CT scan (computed tomography) image dataset to predict and classify strokes.

Topics & Concepts

Deep learningStroke (engine)Convolutional neural networkArtificial intelligenceComputer scienceMachine learningNeuroimagingBrain diseaseDiseaseArtificial neural networkComputed tomographyMedicineRadiologyPathologyEngineeringPsychiatryMechanical engineeringBrain Tumor Detection and ClassificationAdvanced Neural Network ApplicationsAI in cancer detection