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350770

OPTIMIZING MRI-BASED MEDICAL DIAGNOSIS: COMPARATIVE ANALYSIS OF EFFICIENTNET PERFORMANCE WITH VARYING LEARNING RATES

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Last updated: 25 Dec 2024

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Abstract

Magnetic Resonance Imaging (MRI) has emerged as a fundamental tool in the field of medical diagnostics, offering detailed insights into anatomical structures. As the demand for efficient and accurate diagnosis increases, leveraging deep learning techniques becomes imperative, among which the learning rate stands out as a pivotal factor influencing model convergence and generalization. In this research, we investigate the influence of varying learning rates on the efficacy of the EfficientNet B0 model, a cutting-edge convolutional neural network design acclaimed for its efficiency and proficiency in tasks related to image classification. Our comparative analysis unveils the profound influence of learning rates on the diagnostic accuracy and efficiency of the model. Specifically, we observe that optimal learning rates significantly enhance the convergence speed and overall performance of EfficientNet in medical image.
 
In conclusion, this research highlighting the importance of learning rates in improving diagnostic precision and efficacy. We observed a wide range of outcomes in terms of training and validation accuracy, as well as training and validation losses. Notably, Trial 1 and Trial 2, which utilized lower initial learning rates (0.001 and 0.01, respectively), achieved higher validation accuracy compared to Trial 3, where the initial learning rate was set to 0.1. This suggests that tuning learning rates may lead to better convergence and generalization in the training process

DOI

10.21608/jest.2024.278570.1085

Keywords

MRI, EfficientNet, classification models, learning rates, medical diagnosis

Authors

First Name

Wael

Last Name

Ahmed

MiddleName

Abouelwafa

Affiliation

Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, 61519, EGYPT.

Email

wael.wafa@minia.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Massoud

MiddleName

Ahmed

Affiliation

Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, 61519, EGYPT.

Email

dr.massoud@mu.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

El-Bouridy

MiddleName

E.

Affiliation

Alexandria Higher Institute of Engineering &Technology, Alex, Egypt.

Email

mohamed.elbouridy@aiet.edu.eg

City

-

Orcid

-

Volume

21

Article Issue

2

Related Issue

47200

Issue Date

2024-04-01

Receive Date

2024-03-22

Publish Date

2024-04-01

Page Start

105

Page End

119

Print ISSN

2090-5882

Online ISSN

2090-5955

Article File

JEST_Volume 21_Issue 2_Pages 105-119.pdf

PDF . 947.8kB

Link

https://jest.journals.ekb.eg/article_350770.html

Detail API

https://jest.journals.ekb.eg/service?article_code=350770

Order

350,770

Type

Original Article

Type Code

1,211

Publication Type

Journal

Publication Title

Journal of the Egyptian Society of Tribology

Publication Link

https://jest.journals.ekb.eg/

MainTitle

OPTIMIZING MRI-BASED MEDICAL DIAGNOSIS: COMPARATIVE ANALYSIS OF EFFICIENTNET PERFORMANCE WITH VARYING LEARNING RATES

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Article

Created At

25 Dec 2024