• Vidya D.Argade
• Diksha Gautam
• Raman Vishwakarma
• Diksha Singh

Stroke and seizure disorders are critical neurological conditions that require rapid and accurate diagnosis for effective treatment. Traditional diagnostic methods involve neuroimaging modalities such as CT scan, but the manual interpretation of these images can be time-consuming a and prone to error. Recently, machine learning (ML) techniques have shown great promise in streamlining neuroimage analysis, resulting in quicker and more precise diagnoses, which enhances patient outcomes This presents a comprehensive framework that applies machine learning (ML) techniques to neuroimaging data for the detection of strokes and seizures. There are several steps in the framework: preprocessing the data, feature extraction, model selection, training, and evaluation. These steps concentrate on utilizing convolutional neural networks (CNNs), along with machine learning and ensemble learning methods. Additionally, we address the challenges of dataset variability, interpretability, and the integration of multimodal imaging data

Stroke, feature selection , genetic algorithm , LSTM, BiLSTM, CT images and CNN

"Neuroimage Analysis for Stroke Detection: A Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.f728-f732, October-2024, Available :http://www.jetir.org/papers/JETIR2410579.pdf

"Neuroimage Analysis for Stroke Detection: A Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppf728-f732, October-2024, Available at : http://www.jetir.org/papers/JETIR2410579.pdf