Profile URL: https://hdl.handle.net/20.500.13091/14827
Job Title:Dr. Öğr. Gör.
Email Address:mcergene@ktun.edu.tr
Main Affiliation:01.01. Other Departments
Status: Current Staff
ORCID:
0000-0003-1167-146X
0000-0003-1167-146XScopus ID:
57193738202
57193738202YÖK Akademik: 108F7C3D1C7A8DF5
Google Scholar:
wbG48SEAAAAJ
wbG48SEAAAAJWeb of Science ID:
AAH-3903-2021
AAH-3903-2021Name Variants:
Ergene, Mehmet C. Ergene, M. Celalettin
9 results
Scholarly Output Search Results
Now showing 1 - 9 of 9
Other Article Profesyonel futbolcuların sakatlık iyileşiminin kızılötesi termografi ile takibi: Ön Çalışma(2020) Ergene, Mehmet Celalettin; Bayrak, Ahmet; Ceylan, MuratInfrared thermography is a non-invasive method of translating and viewing the radiating heat from the target surface collected by the infrared sensor into temperature as a digital image. Infrared thermography contains very useful data, especially for medical reasons. However, it has been accepted just recently. Although its usage is still questioned in sports medicine, recent studies claimed that infrared thermography can be used to examine muscle problems, injuries, and joint problems, etc. Sports medicine, physiotherapists, and medical imaging have vital importance for football teams and its’ success. During a season football teams lose a lot of matches because of the high rate of injury. That is why preventing an injury has more importance for a football team than healing an existing injury. In consequence physiotherapists use many methods to prevent football players from being injured and monitorise the injury such as creatine kinase tests, muscle strength measurements, MRI, etc. However, these methods are either not enough successful or expensive. In our study, we have developed an image processing software to examine lower extremities muscle problems of football players when they occur and after they rested a day. With this software, we aim to help physiotherapists to regulate rehabilitation plan and decide when to end the rehabilitation. Thanks to this, physiotherapists can decide to rest the football players or start treatment so that they will not get injured unnecessarily and will have a lower risk of injury. Thorough this the success of the football teams will increase because the football players will not miss matches because of the extreme training or overlooked injuries. In our proposed method, with infrared thermography, 3 football players with documented injuries were observed. They have studied again after the football players rested for 1 day and the findings were analyzed. For that the thermographic color palette’s RGB values are calculated in such a way that the upper and lower color values are discovered. In the next step, a binary mask is created, and this mask is blended with the grayscale original image, and the areas with muscle problems are displayed colored so that the physiotherapists can detect and examine problems easier. In the results part, it is shown that the areas are detected better than the human eye. It is concluded that with the help of the image processing algorithm muscle problems are detected successfully and the healing process after the resting is observed.Article Citation - WoS: 2Citation - Scopus: 2A New Deep Learning Based End-To Pipeline for Hamstring Injury Detection in Thermal Images of Professional Football Player(Taylor & Francis Ltd, 2024-07-05) Ergene, Mehmet Celalettin; Bayrak, Ahmet; Ceylan, MuratFootball clubs use various methods such as thermal imaging which is a non-invasive and faster method to detect injuries and increase the success rate of the football club by reducing the injury rate. Studies have proven that with thermal imaging it is possible to detect inflammation caused by an injury. Therefore, it is possible to detect potential injury with infrared thermography. One of the biggest handicaps of injury detection with thermal imaging is that it is open to subjective interpretation, there are many points that can be missed, and it takes time to analyse them one by one. In order to avoid this problem, to increase the success of injury detection, a deep learning supported pipeline has been designed in this study to detect injuries from thermal images. In this pipeline, the hamstring muscle region from the football player thermal images was segmented using U-Net architecture. After that in order to detect injuries, segmented muscle region is classified by using Densenet, Resnet, VGG, Efficientnet architectures variations and feature pyramid added at the end of these architectures. Among the architectures used for classification, the EfficientnetB0 and EfficientnetB1+feature pyramid architectures are the most successful, with accuracies of 83.9% and 81%, respectively.Article Citation - WoS: 2Citation - Scopus: 3Thermography Method Under the Influence of Exercise in the Detection of Muscle Injuries: Sartorius Muscle Case Report(Churchill Livingstone, 2024) Bayrak, A.; Ergene, M.C.; Ceylan, M.Background: The aim of this study was to determine the level of participation in the training of the athlete who applied to the clinic with pain by infrared thermography. Symptoms of sartorius muscle (SM) injury are like rectus femoris injuries. Case scenario: Grade I SM injury of a 23-year-old male football player was determined by thermographic diagnosis. Taking a resting thermal image before the training of the player reported a pain in the upper thigh region. Outcomes: Since both legs were equally loaded, in accordance with the method we developed, the thermal image was taken again after a 10-min cycling program with 30–40% resistance. The heat maps of legs seen in the pre- and post-training images were analyzed. There was no asymmetrical finding indicating injury in the resting thermographic evaluation, but asymmetric findings showing the injury in the region of SM were obtained in the repeated thermographic imaging after the 10-min cycling program. Grade I SM injury was detected by MRI afterwards. Conclusion: Even if there is no sign of asymmetry in the resting thermography of football players having signs of pain, the injured muscle should be provoked with a safe exercise program and the thermal image should be retaken. © 2024 Elsevier LtdConference Object Citation - Scopus: 2Evaluation of Deep Learning Models for Lower Extremity Muscle Segmentation in Thermal Imaging(Springer Science and Business Media Deutschland GmbH, 2023) Ergene, M.C.; Bayrak, A.; Çevik, M.; Ceylan, M.Competition and market size in sports are constantly increasing. In this case, one of the biggest problems of sports clubs is athlete injuries. Especially in football, athlete injury costs are very high. However, most injuries are non-contact and preventable. Sports medicine specialists utilise many medical imaging methods for the prevention of sports injuries. Thermography is an imaging method that has been used in the examination of sports injuries in recent years. Fast and accurate segmentation of muscle regions in thermal images enables more objective analyses. In this study, lower extremity thermal images were taken from football players of a super league club for a certain period of time. From these raw thermal images, 9 different muscle groups of the athletes were labelled and a dataset was created. U-Net, FPN, Linknet and PSPNet segmentation models were trained with this dataset. IoU, F1, Precision, Recall, Precision, Recall evaluation metrics were used to evaluate these models. In the separate models trained for each muscle group, the IoU value achieved over 95% success. When the results of the study are analysed, it is discussed that these segmentation models can be used as a critical tool in injury analysis and evaluation in athletes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Article Monitoring the Reactions of Athletes With History of Rectus Femoris Proximal Tear Healed With Different Methods To Training Load With Thermography(2023-08-22) Bayrak, Ahmet; Ergene, Mehmet Celalettin; Ceylan, MuratAlthough rectus femoris (RF) injuries are rare, it is an important muscle that should be considered because of its contribution to actions such as shooting and fast running in football. In the literature, there is no consensus on which conservative or surgical methods should be preferred in RF total rupture. Although MRI is the gold standard method in the detection of injury, there is a controversy in the literature for post-injury imaging and follow-up. In addition, there is a lack of diagnostic imaging methods in the literature on how training load affects athletes. In current study, the effect of training load on athletes is evaluated by thermography after treatment of the RF muscle with different methods. This study is worthy of being a case report in terms of providing evidence on how the training load affects the sports lives of athletes who return to sports after surgery or conservative treatment.Article The Use of Prototypical and Siamese Networks in the Determination of Lower Extremity Injuries in Professional Football Players with Thermographic Data(Taylor & Francis Ltd, 2025-11-28) Ergene, Mehmet Celalettin; Bayrak, Ahmet; Ceylan, MuratEarly diagnosis of lower extremity injuries in professional football players is crucial for maintaining performance and minimising long-term risks. Despite the growing use of thermographic imaging as a non-invasive tool for detecting musculoskeletal disorders, its integration into automated injury detection systems remains limited, particularly under data-scarce conditions. Given the need for effective early detection methods and the potential of thermography in sports medicine, this study investigates the applicability of deep learning models for classifying lower extremity injuries. Specifically, it evaluates the performance of Prototypical Network and Siamese Network models using thermographic data collected from professional athletes. The original dataset consists of images from 16 healthy and 9 injured individuals, and through augmentation it was expanded to 360 healthy and 180 injured samples. The Prototypical Network achieved an accuracy of 97.78%, while the Siamese Network attained 94%. These findings indicate that both models are capable of accurate injury detection, despite challenges posed by class imbalance and limited data availability. In conclusion, the study highlights the effectiveness of thermographic imaging combined with deep metric learning in identifying injuries in professional football players and suggests that reliable results can be achieved even in constrained data environments.Article Tracking the Injury Recovery of Professional Football Players With Infrared Thermography: Preliminary Study(2020) Ergene, Mehmet Celalettin; Bayrak, Ahmet; Ceylan, MuratInfrared thermography is a non-invasive method of translating and viewing the radiating heat from the target surface collected by the infrared sensor into temperature as a digital image. Infrared thermography contains very useful data, especially for medical reasons. However, it has been accepted just recently. Although its usage is still questioned in sports medicine, recent studies claimed that infrared thermography can be used to examine muscle problems, injuries, and joint problems, etc. Sports medicine, physiotherapists, and medical imaging have vital importance for football teams and its’ success. During a season football teams lose a lot of matches because of the high rate of injury. That is why preventing an injury has more importance for a football team than healing an existing injury. In consequence physiotherapists use many methods to prevent football players from being injured and monitorise the injury such as creatine kinase tests, muscle strength measurements, MRI, etc. However, these methods are either not enough successful or expensive. In our study, we have developed an image processing software to examine lower extremities muscle problems of football players when they occur and after they rested a day. With this software, we aim to help physiotherapists to regulate rehabilitation plan and decide when to end the rehabilitation. Thanks to this, physiotherapists can decide to rest the football players or start treatment so that they will not get injured unnecessarily and will have a lower risk of injury. Thorough this the success of the football teams will increase because the football players will not miss matches because of the extreme training or overlooked injuries. In our proposed method, with infrared thermography, 3 football players with documented injuries were observed. They have studied again after the football players rested for 1 day and the findings were analyzed. For that the thermographic color palette’s RGB values are calculated in such a way that the upper and lower color values are discovered. In the next step, a binary mask is created, and this mask is blended with the grayscale original image, and the areas with muscle problems are displayed colored so that the physiotherapists can detect and examine problems easier. In the results part, it is shown that the areas are detected better than the human eye. It is concluded that with the help of the image processing algorithm muscle problems are detected successfully and the healing process after the resting is observed.Article Design of a Distributed Control System With Fuzzy Logic Controller and Plc in Wireless Sensor Network Based Industrial Environments and Monitoring the System With Rfid(2019-01-31) Durdu, Akif; Bozkurt, Üzeyir İlbay; Ergene, Mehmet Celalettin; Bozkurt, İlbayNowadays industrial applications are built on automatic control systems. The main reason is to convert these control systems and industrial factories into smart ones. By converting them to smart factories, a high-efficiency rate can be acquired. Generally, automatic control systems are controlled by classical logic via PLC (Programmable Logic Controllers). In this method, many problems can be met with. The most important one of these is that it is complicated to create a numerical control unit. Trying to control the system with the traditional way without creating a model can lead us to complicated algorithms. At the same time, complicated algorithms can cause wrong orientations in the system. However, the fuzzy logic, which is one of the intelligent control methods, can help us to create the system just with linguistic expressions and some rules without requiring a mathematical model. In this way with smart control methods, efficiency can be obtained in factories. Besides control systems, factories can be made smart. RFID technology is the central element of this process. The factorymaterial communication is provided via RFID technology. Thus, factories can communicate with the material that is produced without any human intervention. The development of smart factories and the rapid improvements in automation systems caused demands in wireless technology to rise. As a result of these demands, wireless sensor networks became a critical subject, and its use is widespread. These systems provide us to send the data in a certain distance without any loss of the data, also removes all the cables in the workplace. In this study, a control system, distributed in a wireless sensor network based industrial places, is controlled by a traditional PLC method and fuzzy logic. At the same time, the system is monitored by RFID and a solution is proposed to a smart factory application.
Research Topics
Domains
Health SciencesPhysical Sciences
Fields
MedicineComputer ScienceEngineering
Subfields
Radiology, Nuclear Medicine and ImagingOrthopedics and Sports MedicineHuman-Computer InteractionMechanics of Materials
Specific Research Areas
Infrared Thermography in Medicine
Sports injuries and prevention
Sports Performance and Training
Hand Gesture Recognition Systems
Thermography and Photoacoustic Techniques
Sustainable Development Goals
16PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
Documents
7
Citations
29
h-index
3
Documents
6
Citations
18
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Selçuk University | 8 |
| Konya Technical University | 8 |
| Optech (Canada) | 1 |
| Würth Elektronik (Germany) | 1 |
1 / 1
Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Quantitative Infrared Thermography Journal | 2 |
| European Journal of Science and Technology | 1 |
| Journal of Bodywork and Movement Therapies | 1 |
| Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 1 |
| SSRN Electronic Journal | 1 |
Current Page: 1 / 2
Scholarly Output
9
Articles
7
Views / Downloads
14/12
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
4
Scopus Citation Count
7
Patents
0
Projects
0
WoS Citations per Publication
0.44
Scopus Citations per Publication
0.78
Open Access Source
5
Supervised Theses
0
Scopus Quartile Distribution
Competency Cloud
