Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

<Previous Article In Volume
Next Article In Volume>

Optimizing Ischemic Stroke Diagnosis: Enhanced Performance with MobileNetV2 in Automated Image Segmentation

Authors
K. Devi Priya1, *, Boddu Jahnavi1, Patibandla Savithri1
1Department of Computer Science and EngineeringLakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram, India Affiliated to JNTUK, Kakinada, Andhra Pradesh, India
*Corresponding author. Email: k.devipriya20@gmail.com
Corresponding Author
K. Devi Priya
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_13How to use a DOI?
Keywords
Deep Learning; VGG16; VGG19; ResNet50; MobileNetV2; Ischemic Stroke; Computed Tomography (CT)
Abstract

Early identification of ischemic stroke leads to a speedy recovery from severe repercussions and irreversible brain damage. Stroke affects people differently, with varying experiences during the event and variable paths to recovery afterward. Radiologists utilize CT (Computed Tomography) scans to diagnose stroke patients, but occasionally they struggle to spot abnormalities in the pictures. Computer-aided diagnosis, (CAD), is a crucial component of medical image analysis that enables radiologists to quickly assess and interpret abnormalities. Using CNN deep learning techniques, our research aims to establish an automated approach for diagnosing ischemic strokes in their early stages. Based on CNN models such as VGG16, VGG19, ResNet50 and MobileNetV2, our suggested methodology divides brain stroke CT (Computerized Tomography) images into ischemic and non-ischemic images.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_13
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_13How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. Devi Priya
AU  - Boddu Jahnavi
AU  - Patibandla Savithri
PY  - 2024
DA  - 2024/07/30
TI  - Optimizing Ischemic Stroke Diagnosis: Enhanced Performance with MobileNetV2 in Automated Image Segmentation
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 130
EP  - 138
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_13
DO  - 10.2991/978-94-6463-471-6_13
ID  - Priya2024
ER  -