Diabetic foot ulcer detection using deep learning approaches
The most recurrent side effect of diabetes is diabetic foot ulcers and if unattended cause imputations. Diabetic feet affect 15% to 25% of diabetic people globally. Diabetes complications are due to less or no awareness of the consequences of diabetes among diabetic patients. Technology leveraging i...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-01-01
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| Series: | Sensors International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666351122000559 |
| _version_ | 1827367099077492736 |
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| author | Puneeth N. Thotad Geeta R. Bharamagoudar Basavaraj S. Anami |
| author_facet | Puneeth N. Thotad Geeta R. Bharamagoudar Basavaraj S. Anami |
| author_sort | Puneeth N. Thotad |
| collection | DOAJ |
| description | The most recurrent side effect of diabetes is diabetic foot ulcers and if unattended cause imputations. Diabetic feet affect 15% to 25% of diabetic people globally. Diabetes complications are due to less or no awareness of the consequences of diabetes among diabetic patients. Technology leveraging is an attempt to create distinct, affordable, and simple diabetic foot diagnostic strategies for patients and doctors. This work proposes early detection and prognosis of diabetic foot ulcers using the EfficientNet, a deep neural network model. EfficientNet is applied to an image set of 844-foot images, composed of healthy and diabetic ulcer feet. Better performance is obtained compared to earlier models using EfficientNet by carefully balancing network width, depth, and image resolution. The EfficientNet performed better compared to popular models like AlexNet, GoogleNet, VGG16, and VGG19. It gave maximum accuracy, f1-score, recall, and precision of 98.97%, 98%, 98%, and 99%, respectively. |
| first_indexed | 2024-03-08T09:01:41Z |
| format | Article |
| id | doaj.art-98f7a006568b4067b21b10cb605db535 |
| institution | Directory Open Access Journal |
| issn | 2666-3511 |
| language | English |
| last_indexed | 2024-03-08T09:01:41Z |
| publishDate | 2023-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Sensors International |
| spelling | doaj.art-98f7a006568b4067b21b10cb605db5352024-02-01T06:35:15ZengKeAi Communications Co., Ltd.Sensors International2666-35112023-01-014100210Diabetic foot ulcer detection using deep learning approachesPuneeth N. Thotad0Geeta R. Bharamagoudar1Basavaraj S. Anami2Department of Master of Computer Applications, KLE Institute of Technology Hubballi, 580027, India; Visvesvaraya Technological University, Jnana Sangama, Belagavi, 590018, IndiaDepartment of Computer Science & Engineering, KLE Institute of Technology Hubballi, 580027, IndiaSchool of Computer Science and Engineering, KLE Technological University, Hubballi, 580031, India; Corresponding author.The most recurrent side effect of diabetes is diabetic foot ulcers and if unattended cause imputations. Diabetic feet affect 15% to 25% of diabetic people globally. Diabetes complications are due to less or no awareness of the consequences of diabetes among diabetic patients. Technology leveraging is an attempt to create distinct, affordable, and simple diabetic foot diagnostic strategies for patients and doctors. This work proposes early detection and prognosis of diabetic foot ulcers using the EfficientNet, a deep neural network model. EfficientNet is applied to an image set of 844-foot images, composed of healthy and diabetic ulcer feet. Better performance is obtained compared to earlier models using EfficientNet by carefully balancing network width, depth, and image resolution. The EfficientNet performed better compared to popular models like AlexNet, GoogleNet, VGG16, and VGG19. It gave maximum accuracy, f1-score, recall, and precision of 98.97%, 98%, 98%, and 99%, respectively.http://www.sciencedirect.com/science/article/pii/S2666351122000559Deep learningConvolutional neural networkDiabetic foot ulcerDigital healthcareDiabetics |
| spellingShingle | Puneeth N. Thotad Geeta R. Bharamagoudar Basavaraj S. Anami Diabetic foot ulcer detection using deep learning approaches Sensors International Deep learning Convolutional neural network Diabetic foot ulcer Digital healthcare Diabetics |
| title | Diabetic foot ulcer detection using deep learning approaches |
| title_full | Diabetic foot ulcer detection using deep learning approaches |
| title_fullStr | Diabetic foot ulcer detection using deep learning approaches |
| title_full_unstemmed | Diabetic foot ulcer detection using deep learning approaches |
| title_short | Diabetic foot ulcer detection using deep learning approaches |
| title_sort | diabetic foot ulcer detection using deep learning approaches |
| topic | Deep learning Convolutional neural network Diabetic foot ulcer Digital healthcare Diabetics |
| url | http://www.sciencedirect.com/science/article/pii/S2666351122000559 |
| work_keys_str_mv | AT puneethnthotad diabeticfootulcerdetectionusingdeeplearningapproaches AT geetarbharamagoudar diabeticfootulcerdetectionusingdeeplearningapproaches AT basavarajsanami diabeticfootulcerdetectionusingdeeplearningapproaches |