A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images
Chronic venous insufficiency (CVI) is a serious disease characterised by the inability of the veins to effectively return blood from the legs back to the heart. This condition represents a significant public health issue due to its prevalence and impact on quality of life. In this work, we propose a...
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| Language: | English |
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Kaunas University of Technology
2024-12-01
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| Series: | Elektronika ir Elektrotechnika |
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| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/38394 |
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| author | Talha Karadeniz Gul Tokdemir H. Hakan Maras |
| author_facet | Talha Karadeniz Gul Tokdemir H. Hakan Maras |
| author_sort | Talha Karadeniz |
| collection | DOAJ |
| description | Chronic venous insufficiency (CVI) is a serious disease characterised by the inability of the veins to effectively return blood from the legs back to the heart. This condition represents a significant public health issue due to its prevalence and impact on quality of life. In this work, we propose a tool to help doctors effectively diagnose CVI. Our research is based on extracting Visual Geometry Group network 16 (VGG-16) features and integrating a new classifier, which exploits mean absolute deviation (MAD) statistics to classify samples. Although simple in its core, it outperforms state-of-the-art method which is known as the CVI-classifier in the literature, and additionally it performs better than the methods such as multi-layer perceptron (MLP), Naive Bayes (NB), and gradient boosting machines (GBM) in the context of VGG-based classification of CVI. We had 0.931 accuracy, 0.888 Kappa score, and 0.916 F1-score on a publicly available CVI dataset which outperforms the state-of-the-art CVI-classifier having 0.909, 0.873, and 0.900 for accuracy, Kappa score, and F1-score, respectively. Additionally, we have shown that our classifier has a generalisation capacity comparable to support vector machines (SVM), by conducting experiments on eight different datasets. In these experiments, it was observed that our classifier took the lead on metrics such as F1-score, Kappa score, and receiver operating characteristic area under the curve (ROC AUC). |
| format | Article |
| id | doaj-art-a5c38c8db95648eb9a9c027c493e6657 |
| institution | Kabale University |
| issn | 1392-1215 2029-5731 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj-art-a5c38c8db95648eb9a9c027c493e66572025-01-07T13:37:57ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312024-12-01306374410.5755/j02.eie.3839443648A Classifier for Automatic Categorisation of Chronic Venous Insufficiency ImagesTalha Karadeniz0Gul Tokdemir1H. Hakan Maras2Department of Software Engineering, Cankaya University, Ankara, TurkiyeDepartment of Computer Engineering, Cankaya University, Ankara, TurkiyeDepartment of Computer Programming, Cankaya University, Ankara, TurkiyeChronic venous insufficiency (CVI) is a serious disease characterised by the inability of the veins to effectively return blood from the legs back to the heart. This condition represents a significant public health issue due to its prevalence and impact on quality of life. In this work, we propose a tool to help doctors effectively diagnose CVI. Our research is based on extracting Visual Geometry Group network 16 (VGG-16) features and integrating a new classifier, which exploits mean absolute deviation (MAD) statistics to classify samples. Although simple in its core, it outperforms state-of-the-art method which is known as the CVI-classifier in the literature, and additionally it performs better than the methods such as multi-layer perceptron (MLP), Naive Bayes (NB), and gradient boosting machines (GBM) in the context of VGG-based classification of CVI. We had 0.931 accuracy, 0.888 Kappa score, and 0.916 F1-score on a publicly available CVI dataset which outperforms the state-of-the-art CVI-classifier having 0.909, 0.873, and 0.900 for accuracy, Kappa score, and F1-score, respectively. Additionally, we have shown that our classifier has a generalisation capacity comparable to support vector machines (SVM), by conducting experiments on eight different datasets. In these experiments, it was observed that our classifier took the lead on metrics such as F1-score, Kappa score, and receiver operating characteristic area under the curve (ROC AUC).https://eejournal.ktu.lt/index.php/elt/article/view/38394classification algorithmsdecision support systemsparticle swarm optimisation |
| spellingShingle | Talha Karadeniz Gul Tokdemir H. Hakan Maras A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images Elektronika ir Elektrotechnika classification algorithms decision support systems particle swarm optimisation |
| title | A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images |
| title_full | A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images |
| title_fullStr | A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images |
| title_full_unstemmed | A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images |
| title_short | A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images |
| title_sort | classifier for automatic categorisation of chronic venous insufficiency images |
| topic | classification algorithms decision support systems particle swarm optimisation |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/38394 |
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