Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network
The most widespread illness of the diabetic eye that causes missing eye vision is diabetic retinopathy (DR), which requires disclosure soon to prevent the vision loss of the sick. In this study, two features are extracted from retina images with for multiclass DR classification, which include color-...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804768/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850037738612457472 |
|---|---|
| author | Ammar Jawad Kadhim Hadi Seyedarabi Reza Afrouzian Fadhil Sahib Hasan |
| author_facet | Ammar Jawad Kadhim Hadi Seyedarabi Reza Afrouzian Fadhil Sahib Hasan |
| author_sort | Ammar Jawad Kadhim |
| collection | DOAJ |
| description | The most widespread illness of the diabetic eye that causes missing eye vision is diabetic retinopathy (DR), which requires disclosure soon to prevent the vision loss of the sick. In this study, two features are extracted from retina images with for multiclass DR classification, which include color-based Blood Vessel (BV) segmentation and color-based Contrast-Limited-Adaptive-Histogram-Equalization-Top-Hat (CLAH-TH) segmentation. These features are integrated to enhance the accuracy of classification and detection of DR. Variant models, especially VGG19 and InceptionV3, are trained using a transfer learning approach on the proposed extracted features for DR grading. The data augmentation strategy is employed to improve the accuracy and performance of the proposed method by balancing the dataset and aligning the number of images in each class. Experimental results demonstrate that the proposed method outperforms contemporary CNN models when utilizing the suggested features. The best results obtained from experiments on the Kaggle DR database using the pretrained VGG19 model include an accuracy of 96.7%, a sensitivity of 0.971, and a specificity of 0.981. |
| format | Article |
| id | doaj-art-3b1a53df21374bcba918b9121cf1f411 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3b1a53df21374bcba918b9121cf1f4112025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219475019476110.1109/ACCESS.2024.351936110804768Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural NetworkAmmar Jawad Kadhim0https://orcid.org/0009-0009-5170-5805Hadi Seyedarabi1https://orcid.org/0000-0001-6652-2467Reza Afrouzian2https://orcid.org/0000-0002-6968-0409Fadhil Sahib Hasan3https://orcid.org/0000-0002-1550-6974Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranMiyaneh Faculty of Engineering, University of Tabriz, Miyaneh, IranElectrical Engineering Department, Mustansiriyah University, Baghdad, IraqThe most widespread illness of the diabetic eye that causes missing eye vision is diabetic retinopathy (DR), which requires disclosure soon to prevent the vision loss of the sick. In this study, two features are extracted from retina images with for multiclass DR classification, which include color-based Blood Vessel (BV) segmentation and color-based Contrast-Limited-Adaptive-Histogram-Equalization-Top-Hat (CLAH-TH) segmentation. These features are integrated to enhance the accuracy of classification and detection of DR. Variant models, especially VGG19 and InceptionV3, are trained using a transfer learning approach on the proposed extracted features for DR grading. The data augmentation strategy is employed to improve the accuracy and performance of the proposed method by balancing the dataset and aligning the number of images in each class. Experimental results demonstrate that the proposed method outperforms contemporary CNN models when utilizing the suggested features. The best results obtained from experiments on the Kaggle DR database using the pretrained VGG19 model include an accuracy of 96.7%, a sensitivity of 0.971, and a specificity of 0.981.https://ieeexplore.ieee.org/document/10804768/Diabetic retinopathyfeature extractionpretrained VGG19pretrained InceptionV3contrast-limited-adaptive-histogram-equalizationmulticlass classification |
| spellingShingle | Ammar Jawad Kadhim Hadi Seyedarabi Reza Afrouzian Fadhil Sahib Hasan Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network IEEE Access Diabetic retinopathy feature extraction pretrained VGG19 pretrained InceptionV3 contrast-limited-adaptive-histogram-equalization multiclass classification |
| title | Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network |
| title_full | Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network |
| title_fullStr | Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network |
| title_full_unstemmed | Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network |
| title_short | Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network |
| title_sort | diabetic retinopathy classification using hybrid color based clahe and blood vessel in deep convolution neural network |
| topic | Diabetic retinopathy feature extraction pretrained VGG19 pretrained InceptionV3 contrast-limited-adaptive-histogram-equalization multiclass classification |
| url | https://ieeexplore.ieee.org/document/10804768/ |
| work_keys_str_mv | AT ammarjawadkadhim diabeticretinopathyclassificationusinghybridcolorbasedclaheandbloodvesselindeepconvolutionneuralnetwork AT hadiseyedarabi diabeticretinopathyclassificationusinghybridcolorbasedclaheandbloodvesselindeepconvolutionneuralnetwork AT rezaafrouzian diabeticretinopathyclassificationusinghybridcolorbasedclaheandbloodvesselindeepconvolutionneuralnetwork AT fadhilsahibhasan diabeticretinopathyclassificationusinghybridcolorbasedclaheandbloodvesselindeepconvolutionneuralnetwork |