Paper Title

Brain Stroke Detection Using Deep Learning

Article Identifiers

Registration ID: IJNRD_216428

Published ID: IJNRD2404861

DOI: Click Here to Get

Authors

Pagala Parvez , Mr.V.Aravinda Rajan , Gonuguntla Chaitanya , Majjari Sai Siva , Kasarla Uday Kumar

Keywords

CT scans, Brain Structure, Deep learning Convolutional neural network, Image classification,Restnet-50

Abstract

Stroke is a serious medical disorder that needs to be diagnosed and treated quickly in order to reduce long- term effects and enhance patient outcomes. This study uses ResNet-50, a potent convolutional neural network architecture, to provide a unique method for the early diagnosis of brain strokes. By utilizing ResNet-50's deep learning capabilities, the suggested system is able to automatically evaluate medical imaging data, including CT and MRI scans, in order to spot possible stroke symptoms. The well- known ResNet-50 algorithm, which can handle complicated visual data, is customized to the unique properties of brain imaging in order to discriminate between areas of the brain that are healthy and those that have been harmed by a stroke. The medical photos are pre-processed as part of the protocol to improve pertinent characteristics, and finally using a carefully selected dataset of both healthy and stroke-affected brain pictures to train the ResNet- 50 model. The pre-trained ResNet-50 model is refined through the use of transfer learning approaches, improving its capacity to recognize and identify stroke patterns in a variety of patient data.

How To Cite (APA)

Pagala Parvez, Mr.V.Aravinda Rajan, Gonuguntla Chaitanya, Majjari Sai Siva, & Kasarla Uday Kumar (April-2024). Brain Stroke Detection Using Deep Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), i513-i519. https://ijnrd.org/papers/IJNRD2404861.pdf

Issue

Volume 9 Issue 4, April-2024

Pages : i513-i519

Other Publication Details

Paper Reg. ID: IJNRD_216428

Published Paper Id: IJNRD2404861

Downloads: 000121989

Research Area: Computer Science & Technology 

Country: Virudhunagar, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404861.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404861

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

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

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Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

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Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

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