An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Diagnostics Pub Date : 2025-02-25 DOI:10.3390/diagnostics15050551
Madallah Alruwaili, Mahmood Mohamed
{"title":"An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification.","authors":"Madallah Alruwaili, Mahmood Mohamed","doi":"10.3390/diagnostics15050551","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> Medical diagnosis for skin diseases, including leukemia, early skin cancer, benign neoplasms, and alternative disorders, becomes difficult because of external variations among groups of patients. A research goal is to create a fusion-level deep learning model that improves stability and skin disease classification performance. <b>Methods:</b> The model design merges three convolutional neural networks (CNNs): EfficientNet-B0, EfficientNet-B2, and ResNet50, which operate independently under distinct branches. The neural network model uses its capability to extract detailed features from multiple strong architectures to reach accurate results along with tight classification precision. A fusion mechanism completes its operation by transmitting extracted features to dense and dropout layers for generalization and reduced dimensionality. Analyses for this research utilized the 27,153-image Kaggle Skin Diseases Image Dataset, which distributed testing materials into training (80%), validation (10%), and testing (10%) portions for ten skin disorder classes. <b>Results:</b> Evaluation of the proposed model revealed 99.14% accuracy together with excellent precision, recall, and F1-score metrics. <b>Conclusions:</b> The proposed deep learning approach demonstrates strong potential as a starting point for dermatological diagnosis automation since it shows promise for clinical use in skin disease classification.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 5","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11898587/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/diagnostics15050551","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Medical diagnosis for skin diseases, including leukemia, early skin cancer, benign neoplasms, and alternative disorders, becomes difficult because of external variations among groups of patients. A research goal is to create a fusion-level deep learning model that improves stability and skin disease classification performance. Methods: The model design merges three convolutional neural networks (CNNs): EfficientNet-B0, EfficientNet-B2, and ResNet50, which operate independently under distinct branches. The neural network model uses its capability to extract detailed features from multiple strong architectures to reach accurate results along with tight classification precision. A fusion mechanism completes its operation by transmitting extracted features to dense and dropout layers for generalization and reduced dimensionality. Analyses for this research utilized the 27,153-image Kaggle Skin Diseases Image Dataset, which distributed testing materials into training (80%), validation (10%), and testing (10%) portions for ten skin disorder classes. Results: Evaluation of the proposed model revealed 99.14% accuracy together with excellent precision, recall, and F1-score metrics. Conclusions: The proposed deep learning approach demonstrates strong potential as a starting point for dermatological diagnosis automation since it shows promise for clinical use in skin disease classification.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
期刊最新文献
The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection. Pain-Related White-Matter Changes Following Mild Traumatic Brain Injury: A Longitudinal Diffusion Tensor Imaging Pilot Study. Performance Comparison of Two In-House PCR Methods for Detecting Neisseria meningitidis in Asymptomatic Carriers and Antimicrobial Resistance Profiling. Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson's Disease Using Vocal Characteristics: A Comparative Analysis. Comparing ChatGPT 4.0's Performance in Interpreting Thyroid Nodule Ultrasound Reports Using ACR-TI-RADS 2017: Analysis Across Different Levels of Ultrasound User Experience.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1