Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and clas...
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MDPI AG
2023-04-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/2/187 |
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| author | Inamullah Saima Hassan Nabil A. Alrajeh Emad A. Mohammed Shafiullah Khan |
| author_facet | Inamullah Saima Hassan Nabil A. Alrajeh Emad A. Mohammed Shafiullah Khan |
| author_sort | Inamullah |
| collection | DOAJ |
| description | The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble’s overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively. |
| first_indexed | 2024-03-11T02:43:31Z |
| format | Article |
| id | doaj.art-33bb4e43ed0d45e5959e3016c667dc4d |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T02:43:31Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-33bb4e43ed0d45e5959e3016c667dc4d2023-11-18T09:28:56ZengMDPI AGBiomimetics2313-76732023-04-018218710.3390/biomimetics8020187Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic RetinopathyInamullah0Saima Hassan1Nabil A. Alrajeh2Emad A. Mohammed3Shafiullah Khan4Institute of Computing, Kohat University of Science and Technology (KUST), Kohat City 24000, PakistanInstitute of Computing, Kohat University of Science and Technology (KUST), Kohat City 24000, PakistanBiomedical Technology Department, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 1433, Saudi ArabiaDepartment of Engineering, Faculty of Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, CanadaInstitute of Computing, Kohat University of Science and Technology (KUST), Kohat City 24000, PakistanThe medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble’s overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively.https://www.mdpi.com/2313-7673/8/2/187diabetic retinopathyensemble modelsmachine learningdeep learningconvolution neural network |
| spellingShingle | Inamullah Saima Hassan Nabil A. Alrajeh Emad A. Mohammed Shafiullah Khan Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy Biomimetics diabetic retinopathy ensemble models machine learning deep learning convolution neural network |
| title | Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy |
| title_full | Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy |
| title_fullStr | Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy |
| title_full_unstemmed | Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy |
| title_short | Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy |
| title_sort | data diversity in convolutional neural network based ensemble model for diabetic retinopathy |
| topic | diabetic retinopathy ensemble models machine learning deep learning convolution neural network |
| url | https://www.mdpi.com/2313-7673/8/2/187 |
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