Yılmaz, Hakan

Profile URL:
Job Title:Doç. Dr.
Email Address:hyilmaz@ktun.edu.tr
Main Affiliation:10.03. Department of Artificial Intelligence and Machine Learning
Status: Current Staff
Website: https://www.ktun.edu.tr/tr/Universite/PersonelBilgi/?prsnl=Tpu4BdUGiiJmyXeMHOAtYL8phkmriLk72hchKo3ZtWAP8XZXI9MkA1uaulD7CGNmuRTQgCD0OF9sSIBwlG14iA==
ORCID:ORCID Profile0000-0002-8553-388X
Scopus ID:Scopus Profile57203166294
Google Scholar:Google Scholar ProfileHBkXKk8AAAAJ
Name Variants:
Yilmaz, Hakan
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2025 - 20262

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Author of Publication: search.filters.isAuthorOfPublication.a3e327bd-4c3a-4e4a-9847-ff2f24af41f1

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Article
    ScabAI: Cilt Görüntülerinden Uyuz Tespiti için Derin Öğrenme Tabanlı Mobil Uygulama
    (2025-12-31) Can, Zeynep Nida; Baki, Hatice Şevval; Özdem, Mehmet; Yılmaz, Hakan; Çökmez, Tahsin
    Scabies, a contagious skin disease caused by the Sarcoptes scabiei mite, remains a significant public health concern globally. This study aims to develop a mobile application, ScabAI, which uses a deep learning model based on Convolutional Neural Networks (CNNs) to detect scabies from skin images. The model was trained using a dataset of 500 images, divided equally between scabies and non-scabies cases, and achieved high performance metrics, including 96.7% accuracy, 96% sensitivity, 97.3% specificity, and a 96.5% F1 score. These results demonstrate the model’s reliability and effectiveness in detecting scabies, outperforming many existing models. The mobile application allows users to capture or upload images of suspected scabies lesions, providing rapid and accurate preliminary diagnoses. ScabAI offers a practical, user- friendly tool that can be beneficial for both healthcare providers and individuals, supporting early detection, timely treatment, and reducing the risk of disease transmission. This study underscores the potential of integrating artificial intelligence with mobile platforms for improved dermatological care, particularly in resource-limited settings. Future research should focus on expanding the dataset to enhance generalization and exploring additional AI techniques to refine detection accuracy. ScabAI not only contributes to AI-assisted dermatology but also serves as a scalable model for developing similar tools targeting other skin conditions. This innovative approach addresses both clinical needs and user accessibility, advancing healthcare outcomes and public health initiatives.
  • Other
    Evaluating the Efficacy of Deep Learning Models for Distinguishing Spinal Hemangiomas from Common Metastatic Tumors in Across MRI Sequences
    (2026) Karkaş, Ahmet Yasin; Demir, Taha Bedir; Yılmaz, Hakan; Okatar, Furkan; Bayram, Serkan; Akgül, Turgut

Research Topics

Domains
Physical SciencesLife Sciences
Fields
EngineeringNeuroscienceComputer Science
Subfields
Mechanical EngineeringElectrical and Electronic EngineeringCognitive NeuroscienceComputer Vision and Pattern Recognition
Specific Research Areas
Advanced machining processes and optimization
Advanced Machining and Optimization Techniques
EEG and Brain-Computer Interfaces
Digital Imaging for Blood Diseases
Advanced Vision and Imaging

Sustainable Development Goals

GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
Documents

9

Citations

260

h-index

8

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Publication Collaboration

Affiliation Name Count
Karabük University 19
Opole University of Technology 4
Graphic Era University 4
University of Johannesburg 3
Yale University 3
1 / 5
Data obtained from OpenAlex
JournalCount
Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji1
Research Square1
Current Page: 1 / 1
Scholarly Output

2

Articles

1

Views / Downloads

2/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

0

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.00

Open Access Source

2

Supervised Theses

0

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