Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features
Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing bet...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/10/1706 |
| _version_ | 1797600489806757888 |
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| author | Ahlam Shamsan Ebrahim Mohammed Senan Hamzeh Salameh Ahmad Shatnawi |
| author_facet | Ahlam Shamsan Ebrahim Mohammed Senan Hamzeh Salameh Ahmad Shatnawi |
| author_sort | Ahlam Shamsan |
| collection | DOAJ |
| description | Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%. |
| first_indexed | 2024-03-11T03:48:49Z |
| format | Article |
| id | doaj.art-91aeed03a6064895b38842ad765b73e2 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T03:48:49Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-91aeed03a6064895b38842ad765b73e22023-11-18T01:03:50ZengMDPI AGDiagnostics2075-44182023-05-011310170610.3390/diagnostics13101706Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid FeaturesAhlam Shamsan0Ebrahim Mohammed Senan1Hamzeh Salameh Ahmad Shatnawi2Computer Department, Applied College, Najran University, Najran 66462, Saudi ArabiaDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, YemenComputer Department, Applied College, Najran University, Najran 66462, Saudi ArabiaEarly detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.https://www.mdpi.com/2075-4418/13/10/1706DenseNet-121MobileNetANNeye diseasesPCAhandcrafted features |
| spellingShingle | Ahlam Shamsan Ebrahim Mohammed Senan Hamzeh Salameh Ahmad Shatnawi Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features Diagnostics DenseNet-121 MobileNet ANN eye diseases PCA handcrafted features |
| title | Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features |
| title_full | Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features |
| title_fullStr | Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features |
| title_full_unstemmed | Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features |
| title_short | Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features |
| title_sort | automatic classification of colour fundus images for prediction eye disease types based on hybrid features |
| topic | DenseNet-121 MobileNet ANN eye diseases PCA handcrafted features |
| url | https://www.mdpi.com/2075-4418/13/10/1706 |
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