Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images
Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate p...
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MDPI AG
2021-04-01
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| Online Access: | https://www.mdpi.com/2073-8994/13/4/670 |
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| author | Niloy Sikder Mehedi Masud Anupam Kumar Bairagi Abu Shamim Mohammad Arif Abdullah-Al Nahid Hesham A. Alhumyani |
| author_facet | Niloy Sikder Mehedi Masud Anupam Kumar Bairagi Abu Shamim Mohammad Arif Abdullah-Al Nahid Hesham A. Alhumyani |
| author_sort | Niloy Sikder |
| collection | DOAJ |
| description | Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it. |
| first_indexed | 2024-03-10T12:22:25Z |
| format | Article |
| id | doaj.art-9effe72788bf419f8a2f7f7abd8b61cf |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T12:22:25Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-9effe72788bf419f8a2f7f7abd8b61cf2023-11-21T15:24:35ZengMDPI AGSymmetry2073-89942021-04-0113467010.3390/sym13040670Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal ImagesNiloy Sikder0Mehedi Masud1Anupam Kumar Bairagi2Abu Shamim Mohammad Arif3Abdullah-Al Nahid4Hesham A. Alhumyani5Computer Science and Engineering Discipline, Khulna University, Khulna 9208, BangladeshDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaComputer Science and Engineering Discipline, Khulna University, Khulna 9208, BangladeshComputer Science and Engineering Discipline, Khulna University, Khulna 9208, BangladeshElectronics and Communication Engineering Discipline, Khulna University, Khulna 9208, BangladeshDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDiabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.https://www.mdpi.com/2073-8994/13/4/670diabetic retinopathy detectionmedical image analysisimage histogramgray-level co-occurrence matrixgenetic algorithmensemble learning |
| spellingShingle | Niloy Sikder Mehedi Masud Anupam Kumar Bairagi Abu Shamim Mohammad Arif Abdullah-Al Nahid Hesham A. Alhumyani Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images Symmetry diabetic retinopathy detection medical image analysis image histogram gray-level co-occurrence matrix genetic algorithm ensemble learning |
| title | Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images |
| title_full | Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images |
| title_fullStr | Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images |
| title_full_unstemmed | Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images |
| title_short | Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images |
| title_sort | severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images |
| topic | diabetic retinopathy detection medical image analysis image histogram gray-level co-occurrence matrix genetic algorithm ensemble learning |
| url | https://www.mdpi.com/2073-8994/13/4/670 |
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