Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification
Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and pre...
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Elsevier
2025-01-01
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| author | Masoud Muhammed Hassan Halbast Rashid Ismail |
| author_facet | Masoud Muhammed Hassan Halbast Rashid Ismail |
| author_sort | Masoud Muhammed Hassan |
| collection | DOAJ |
| description | Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions. Therefore, there is a need to estimate uncertainty in the model's predictions, which cannot be estimated by classical DL models alone. To address this issue, Bayesian DL methods have been proposed, and their use is increasing in the field. In this paper, we developed a straightforward architecture for the classification of DR using a Convolutional Neural Network (CNN) model. We then applied the Bayesian CNN twice, once using Variational Inference (VI) and once using Monte Carlo dropout (MC-dropout) methods, to the same CNN architecture. This allowed us to gain the posterior predictive distributions for each of them. The performance of the proposed models was evaluated on two benchmark datasets, namely APTOS 2019 and Messidor-2. Experimental findings demonstrated that the proposed models surpassed other state-of-the-art models, achieving a test accuracy of 94.70 % and 77.00 % for CNN, 94.00 % and 86.00 % for BCNN-VI, and 93.30 % and 81.00 % for BCNN-MC-dropout on the APTOS dataset and Messidor-2 dataset, respectively. Finally, we computed the entropy and standard deviation on the obtained predictive distribution to quantify the model uncertainty. This research highlights the potential benefits of using Bayesian DL methods in medical image analysis to improve the accuracy and reliability of diagnosing disease and treatment. |
| format | Article |
| id | doaj-art-c68e130cea2b4aba927a699dc95503ec |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-c68e130cea2b4aba927a699dc95503ec2025-02-02T05:28:14ZengElsevierHeliyon2405-84402025-01-01112e41802Bayesian deep learning applied to diabetic retinopathy with uncertainty quantificationMasoud Muhammed Hassan0Halbast Rashid Ismail1Department of Computer Science, College of Science, University of Zakho, Duhok, Kurdistan Regain, Iraq; Department of Computer Science, College of Science, Knowledge University, Erbil, 44001, Iraq; Corresponding author. Department of Computer Science, College of Science, University of Zakho, Duhok, Kurdistan Regain, Iraq.Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, IraqDeep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions. Therefore, there is a need to estimate uncertainty in the model's predictions, which cannot be estimated by classical DL models alone. To address this issue, Bayesian DL methods have been proposed, and their use is increasing in the field. In this paper, we developed a straightforward architecture for the classification of DR using a Convolutional Neural Network (CNN) model. We then applied the Bayesian CNN twice, once using Variational Inference (VI) and once using Monte Carlo dropout (MC-dropout) methods, to the same CNN architecture. This allowed us to gain the posterior predictive distributions for each of them. The performance of the proposed models was evaluated on two benchmark datasets, namely APTOS 2019 and Messidor-2. Experimental findings demonstrated that the proposed models surpassed other state-of-the-art models, achieving a test accuracy of 94.70 % and 77.00 % for CNN, 94.00 % and 86.00 % for BCNN-VI, and 93.30 % and 81.00 % for BCNN-MC-dropout on the APTOS dataset and Messidor-2 dataset, respectively. Finally, we computed the entropy and standard deviation on the obtained predictive distribution to quantify the model uncertainty. This research highlights the potential benefits of using Bayesian DL methods in medical image analysis to improve the accuracy and reliability of diagnosing disease and treatment.http://www.sciencedirect.com/science/article/pii/S2405844025001823Bayesian deep learningConvolutional neural networkUncertainty quantificationDiabetic retinopathy |
| spellingShingle | Masoud Muhammed Hassan Halbast Rashid Ismail Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification Heliyon Bayesian deep learning Convolutional neural network Uncertainty quantification Diabetic retinopathy |
| title | Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| title_full | Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| title_fullStr | Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| title_full_unstemmed | Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| title_short | Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| title_sort | bayesian deep learning applied to diabetic retinopathy with uncertainty quantification |
| topic | Bayesian deep learning Convolutional neural network Uncertainty quantification Diabetic retinopathy |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025001823 |
| work_keys_str_mv | AT masoudmuhammedhassan bayesiandeeplearningappliedtodiabeticretinopathywithuncertaintyquantification AT halbastrashidismail bayesiandeeplearningappliedtodiabeticretinopathywithuncertaintyquantification |