Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model
The incidence of diabetes has increased in recent times due to factors such as obesity and genetic predisposition. Diabetes wears out the eye vessels over time. Diabetic retinopathy (DR) is a serious disease that leads to vision problems. DR can be diagnosed by specialists who examine the fundus ima...
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| Language: | English |
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Çanakkale Onsekiz Mart University
2024-09-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3921746 |
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| author | Cihan Akyel Bünyamin Ciylan |
| author_facet | Cihan Akyel Bünyamin Ciylan |
| author_sort | Cihan Akyel |
| collection | DOAJ |
| description | The incidence of diabetes has increased in recent times due to factors such as obesity and genetic predisposition. Diabetes wears out the eye vessels over time. Diabetic retinopathy (DR) is a serious disease that leads to vision problems. DR can be diagnosed by specialists who examine the fundus images of the eye at regular intervals. With 537 million diabetics in 2021, this method can be time-consuming, costly and inadequate. Artificial intelligence algorithms can provide fast and cost-effective solutions for DR diagnosis. In this study, the noise of blood vessels in fundus images was eliminated using the LinkNet-RCB7 model, and diabetic retinopathy was categorized into five classes using a machine learning-based ensemble model. Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). By using Gray Level Cooccurrence Matrix (GLCM) attributes in the classification phase, a lower resource requirement was aimed for. A Dice coefficient of 85.95% was achieved for the segmentation of blood vessels in the Stare dataset, in addition to 97.46% accuracy for binary classification and 96.10% accuracy for classifying DR into five classes in the dataset APTOS 2019. |
| format | Article |
| id | doaj-art-56668ff5e974488a9c788993d8987416 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-56668ff5e974488a9c788993d89874162025-02-05T18:13:03ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-09-0110356057010.28979/jarnas.1482123453Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble ModelCihan Akyel0https://orcid.org/0000-0003-1792-8254Bünyamin Ciylan1https://orcid.org/0000-0002-6193-2245TC MİLLİ EĞİTİM BAKANLIĞI NECATİBEYGAZI UNIVERSITYThe incidence of diabetes has increased in recent times due to factors such as obesity and genetic predisposition. Diabetes wears out the eye vessels over time. Diabetic retinopathy (DR) is a serious disease that leads to vision problems. DR can be diagnosed by specialists who examine the fundus images of the eye at regular intervals. With 537 million diabetics in 2021, this method can be time-consuming, costly and inadequate. Artificial intelligence algorithms can provide fast and cost-effective solutions for DR diagnosis. In this study, the noise of blood vessels in fundus images was eliminated using the LinkNet-RCB7 model, and diabetic retinopathy was categorized into five classes using a machine learning-based ensemble model. Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). By using Gray Level Cooccurrence Matrix (GLCM) attributes in the classification phase, a lower resource requirement was aimed for. A Dice coefficient of 85.95% was achieved for the segmentation of blood vessels in the Stare dataset, in addition to 97.46% accuracy for binary classification and 96.10% accuracy for classifying DR into five classes in the dataset APTOS 2019.https://dergipark.org.tr/en/download/article-file/3921746diabetic retinopathyensemble learningclassificationsegmentationinformation systems |
| spellingShingle | Cihan Akyel Bünyamin Ciylan Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model Journal of Advanced Research in Natural and Applied Sciences diabetic retinopathy ensemble learning classification segmentation information systems |
| title | Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model |
| title_full | Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model |
| title_fullStr | Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model |
| title_full_unstemmed | Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model |
| title_short | Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model |
| title_sort | blood vessel segmentation and classification of diabetic retinopathy with machine learning based ensemble model |
| topic | diabetic retinopathy ensemble learning classification segmentation information systems |
| url | https://dergipark.org.tr/en/download/article-file/3921746 |
| work_keys_str_mv | AT cihanakyel bloodvesselsegmentationandclassificationofdiabeticretinopathywithmachinelearningbasedensemblemodel AT bunyaminciylan bloodvesselsegmentationandclassificationofdiabeticretinopathywithmachinelearningbasedensemblemodel |