Alexnet architecture variations with transfer learning for classification of wound images
In medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievement...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098623001684 |
| _version_ | 1828717029738151936 |
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| author | Hüseyin Eldem Erkan Ülker Osman Yaşar Işıklı |
| author_facet | Hüseyin Eldem Erkan Ülker Osman Yaşar Işıklı |
| author_sort | Hüseyin Eldem |
| collection | DOAJ |
| description | In medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievements of existing pre-trained architectures for describing (classifying) data belonging to many image sets in the real world are primarily addressed. However, to increase the success of these architectures in a certain area, some improvements and enhancements can be made on the architecture. In this paper, the classification of pressure and diabetic wound images was performed with high accuracy. The six different new AlexNet architecture variations (3Conv_Softmax, 3Conv_SVM, 4Conv_Softmax, 4Conv_SVM, 6Conv_Softmax, 6Conv_SVM) were created with a different number of implementations of Convolution, Pooling, and Rectified Linear Activation (ReLU) layers. Classification performances of the proposed models are investigated by using Softmax classifier and SVM classifier separately. A new original Wound Image Database are created for performance measures. According to the experimental results obtained for the Database, the model with 6 Convolution layers (6Conv_SVM) was the most successful method among the proposed methods with 98.85% accuracy, 98.86% sensitivity, and 99.42% specificity. The 6Conv_SVM model was also tested on diabetic and pressure wound images in the public medetec dataset, and 95.33% accuracy, 95.33% sensitivity, and 97.66% specificity values were obtained. The proposed method provides high performance compared to the pre-trained AlexNet architecture and other state-of-the-art models in the literature. The results showed that the proposed 6Conv_SVM architecture can be used by the relevant departments in the medical world with good performance in medical tasks such as examining and classifying wound images and following up the wound process. |
| first_indexed | 2024-03-12T14:21:02Z |
| format | Article |
| id | doaj.art-fdfd2d6b74f14544a4b4b563f53d8bc5 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-03-12T14:21:02Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-fdfd2d6b74f14544a4b4b563f53d8bc52023-08-19T04:32:07ZengElsevierEngineering Science and Technology, an International Journal2215-09862023-09-0145101490Alexnet architecture variations with transfer learning for classification of wound imagesHüseyin Eldem0Erkan Ülker1Osman Yaşar Işıklı2Vocational School of Technical Sciences, Computer Technologies Department, Karamanoğlu Mehmetbey University, Karaman 70100, Turkey; Corresponding author.Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Konya Technical University, Computer Engineering Department, Konya 42500, TurkeyKaraman Education and Research Hospital, Vascular Surgery Department, Karaman 70100, TurkeyIn medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievements of existing pre-trained architectures for describing (classifying) data belonging to many image sets in the real world are primarily addressed. However, to increase the success of these architectures in a certain area, some improvements and enhancements can be made on the architecture. In this paper, the classification of pressure and diabetic wound images was performed with high accuracy. The six different new AlexNet architecture variations (3Conv_Softmax, 3Conv_SVM, 4Conv_Softmax, 4Conv_SVM, 6Conv_Softmax, 6Conv_SVM) were created with a different number of implementations of Convolution, Pooling, and Rectified Linear Activation (ReLU) layers. Classification performances of the proposed models are investigated by using Softmax classifier and SVM classifier separately. A new original Wound Image Database are created for performance measures. According to the experimental results obtained for the Database, the model with 6 Convolution layers (6Conv_SVM) was the most successful method among the proposed methods with 98.85% accuracy, 98.86% sensitivity, and 99.42% specificity. The 6Conv_SVM model was also tested on diabetic and pressure wound images in the public medetec dataset, and 95.33% accuracy, 95.33% sensitivity, and 97.66% specificity values were obtained. The proposed method provides high performance compared to the pre-trained AlexNet architecture and other state-of-the-art models in the literature. The results showed that the proposed 6Conv_SVM architecture can be used by the relevant departments in the medical world with good performance in medical tasks such as examining and classifying wound images and following up the wound process.http://www.sciencedirect.com/science/article/pii/S2215098623001684Wound image classificationConvolutional Neural Networks (CNN)Deep learningTransfer learningModified AlexNet |
| spellingShingle | Hüseyin Eldem Erkan Ülker Osman Yaşar Işıklı Alexnet architecture variations with transfer learning for classification of wound images Engineering Science and Technology, an International Journal Wound image classification Convolutional Neural Networks (CNN) Deep learning Transfer learning Modified AlexNet |
| title | Alexnet architecture variations with transfer learning for classification of wound images |
| title_full | Alexnet architecture variations with transfer learning for classification of wound images |
| title_fullStr | Alexnet architecture variations with transfer learning for classification of wound images |
| title_full_unstemmed | Alexnet architecture variations with transfer learning for classification of wound images |
| title_short | Alexnet architecture variations with transfer learning for classification of wound images |
| title_sort | alexnet architecture variations with transfer learning for classification of wound images |
| topic | Wound image classification Convolutional Neural Networks (CNN) Deep learning Transfer learning Modified AlexNet |
| url | http://www.sciencedirect.com/science/article/pii/S2215098623001684 |
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