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Skin Disease Classification: A Comparison of ResNet50, MobileNet, and Efficient-B0

Article

Last updated: 01 Feb 2025

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Tags

Computer sciences, artificial intelligence, and robotics

Abstract

Background: Skin diseases are among the most common health issues worldwide, affecting mil-lions of individuals annually. Conditions such as Acne and Rosacea, Eczema, Exan-thems and Drug Eruptions, Scabies, Lyme Disease, Tinea and other fungal infections, and Vasculitis can significantly impact patients' quality of life. Early and accurate diagnosis is crucial for effective treatment and management. However, the diagnosis of skin diseases often requires specialized expertise, which may not be readily availa-ble in all healthcare settings. Misdiagnosis or delayed diagnosis can lead to inappro-priate treatments and worsening conditions. Methods: In this paper, we propose Deep Learning techniques for classifying six common skin diseases. Leveraging the DermNet da-taset, we utilized the EfficientNet-B0 model, known for its accuracy and efficiency, to categorize these dermatological conditions. Our methodology involved data aug-mentation, transfer learning, and fine-tuning the EfficientNet-B0 model. Results: The pro-posed approach achieved an impressive 99% accuracy on the validation set, demon-strating the potential of advanced convolutional neural networks for automated skin disease diagnosis. Furthermore, we compared EfficientNet-B0 with other popular models, including ResNet50 and MobileNet, revealing superior performance both in accuracy and computational efficiency. Specifically, the models achieved accuracy rates of 93%, 94%, and 99% for ResNet50, MobileNet, and EfficientNet-B0, respec-tively. Conclusion: These findings highlight the reliability and effectiveness of the proposed model compared to state-of-the-art approaches.Tumors have been rarely documented in the Arabian dromedary (Camelus dromedarius).

DOI

10.21608/jcmr.2025.327880.1002

Keywords

Dermatological Conditions, Data Augmentation, EfficientNet-B0, Receiver Operating Characteristic

Authors

First Name

Mahmoud

Last Name

Shams

MiddleName

Y.

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

mahmoud.yasin@ai.kfs.edu.eg

City

Kafrelsheikh

Orcid

0000-0003-3021-5902

First Name

Esraa

Last Name

Hassan

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

esraa.hassan@ai.kfs.edu.eg

City

Kafrelsheikh

Orcid

0000-0002-1021-717X

First Name

Sara

Last Name

Gamil

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

gamil677@gmail.com

City

-

Orcid

-

First Name

Aya

Last Name

Ibrahim

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

ayai65291@gmail.com

City

-

Orcid

-

First Name

Esraa

Last Name

Gabr

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

esraagabr115@gmail.com

City

-

Orcid

-

First Name

Sarah

Last Name

Gamal

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

sarahgamal463@gmail.com

City

-

Orcid

-

First Name

Esraa

Last Name

Ibrahim

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

esraaibrahim1115@gmail.com

City

-

Orcid

-

First Name

Fares

Last Name

Abbas

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

faresabbas.ai@gmail.com

City

-

Orcid

-

First Name

Ahmed

Last Name

Mohammed

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

ahmed.hamada.bi@gmail.com

City

-

Orcid

-

First Name

Anwar

Last Name

Khamis

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

anwarkhamishawash@gmail.com

City

-

Orcid

-

First Name

Mohamed

Last Name

Hamed

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

mohamedhammed000111@gmail.com

City

-

Orcid

-

First Name

Mahmoud

Last Name

Mokhtar

MiddleName

-

Affiliation

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Email

mahmoudmokhtar1212001@gmail.com

City

Kafrelsheikh

Orcid

-

First Name

Roheet

Last Name

Bhatnagar

MiddleName

-

Affiliation

Department of Computer Science and Engineering Manipal University Jaipur,Rajasthan, India

Email

roheet.bhatnagar@jaipur.manipal.edu

City

-

Orcid

-

Volume

1

Article Issue

1

Related Issue

53345

Issue Date

2025-01-01

Receive Date

2024-10-18

Publish Date

2025-01-01

Page Start

1

Page End

7

Online ISSN

3062-4614

Article File

JCMR_Volume 1_Issue 1_Pages 1-7.pdf

PDF . 420.7kB

Link

https://jcmr.journals.ekb.eg/article_407474.html

Detail API

http://journals.ekb.eg?_action=service&article_code=407474

Order

407,474

Type

Original research

Type Code

3,282

Publication Type

Journal

Publication Title

Journal of Current Multidisciplinary Research

Publication Link

https://jcmr.journals.ekb.eg/

MainTitle

Skin Disease Classification: A Comparison of ResNet50, MobileNet, and Efficient-B0

Details

Type

Article

Created At

01 Feb 2025