Comparative Analysis of AI-Based Techniques for Brain Stroke Detection: A Review
Keywords:
artificial intelligence, brain stroke detection, comparative analysis, deep learning, machine learningAbstract
In this abstract, various artificial intelligence (AI)-based methods for brain stroke diagnosis are compared and analyzed. Brain strokes, in particular, are the main cause of disability and death worldwide. In recent years, AI algorithms have used deep learning (DL) and machine learning (ML) as viable methods for stroke diagnosis. The performance and evaluation of various ML and DL models are examined and compared in tabular form in this study. The models' computational effectiveness and scalability are compared and presented in tabular form. This study provides valuable insights into the strengths and limitations of various AI, and DL-based techniques for brain stroke detection in tabular form, aiding healthcare professionals and researchers in selecting the most appropriate approach for accurate and efficient stroke diagnosis. In the context of stroke diagnosis, the paper provides an examination of various ML, and DL models for infarct detection. Although these models have an acceptable accuracy of between 70% and 90%, a crucial factor that has been missed is the time complexity associated with applying these models to predict strokes. Furthermore, the study includes a list of open datasets to assist researchers in implementing different models and enhancing their performance.
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