Automated Diabetic Foot Ulcer Detection and Classification Using Deep Learning

Author:

Nagaraju Sunnam1,Kumar Kollati Vijaya2ORCID,Rani B. Prameela3,Lydia E. Laxmi4ORCID,Ishak Mohamad Khairi5,Filali Imen6,Karim Faten Khalid6ORCID,Mostafa Samih M.7ORCID

Affiliation:

1. Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, India

2. Department of Computer Science and Engineering, GITAM School of Technology, Vishakhapatnam Campus, GITAM (Deemed to be a University), Visakhapatnam, India

3. Department of CSE-AIML, Aditya College of Engineering, Surampalem, Andhra Pradesh, India

4. Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam, India

5. Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

6. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

7. Computer Science Department, Faculty of Computers and Information, South Valley University, Qena, Egypt

Funder

Princess Nourah bint Abdulrahman University Researchers

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Diagnostic Accuracy with SE-Inception Model Integration in Pressure Ulcer Detection;Annali Italiani di Chirurgia;2024-08-20

2. Detection of Multi Stage Diabetes Foot Ulcer using Deep Learning Techniques;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

3. SoleScan: Innovating Diabetic Foot Ulcer Identification and Evaluation;2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies;2024-03-22

4. Tailored Deep Learning Approaches for Binary Classification and Evaluation of Diabetic Foot Ulcer Images;2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS);2024-03-14

5. Improving Automated PSN Assessment in Type 2 Diabetes: A Study on Plantar Lesion Recognition and Probe Avoidance Techniques;IEEE Access;2024

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