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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.

Details

Title
Diabetic Foot Ulcer Identification: A Review
Author
Das, Sujit Kumar 1   VIAFID ORCID Logo  ; Roy, Pinki 2 ; Singh, Prabhishek 3   VIAFID ORCID Logo  ; Diwakar, Manoj 4   VIAFID ORCID Logo  ; Singh, Vijendra 5 ; Maurya, Ankur 3 ; Kumar, Sandeep 6   VIAFID ORCID Logo  ; Kadry, Seifedine 7   VIAFID ORCID Logo  ; Kim, Jungeun 8 

 Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar 751030, India 
 Department of Computer Science and Engineering, National Institute of Technology, Silchar 788010, India 
 School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India 
 Computer Science and Engineering Department, Graphic Era Deemed to Be University, Dehradun 248002, India 
 School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India 
 Department of Computer Science & Engineering, Maharaja Surajmal Institute of Technology, Delhi 110058, India 
 Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon; MEU Research Unit, Middle East University, Amman 11831, Jordan 
 Department of Software and CMPSI, Kongju National University, Cheonan 31080, Republic of Korea 
First page
1998
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2829794356
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.