Neuroimage Analysis for Stroke Detection: A Machine Learning Framework

Neuroimage Analysis for Stroke Detection: A Machine Learning Framework

Registration ID: IJNRD_301649

Published ID: IJNRD2410304

DOI: Click Here to Get

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

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

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

Published Paper   E-Certificate

"Neuroimage Analysis for Stroke Detection: A Machine Learning Framework", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.d23-d27, October-2024, Available :https://ijnrd.org/papers/IJNRD2410304.pdf

Volume 9 Issue 10, October-2024

Pages : d23-d27

Paper Reg. ID: IJNRD_301649

Published Paper Id: IJNRD2410304

Downloads: 000121127

Research Area: Science and Technology

Country: Pune, Maharashtra , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2410304.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2410304

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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