A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy
Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work prop...
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| Veröffentlicht in: | Procedia computer science Jg. 235; S. 2090 - 2100 |
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2024
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| Abstract | Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive Jaya Optimization Algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods. |
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| AbstractList | Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive Jaya Optimization Algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods. |
| Author | Rawat, Akhilesh Kumar, Rajeev |
| Author_xml | – sequence: 1 givenname: Akhilesh surname: Rawat fullname: Rawat, Akhilesh organization: Data to Knowledge (D2K) Lab, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, 110 067, India – sequence: 2 givenname: Rajeev surname: Kumar fullname: Kumar, Rajeev email: rajeevkumar.cse@gmail.com organization: Data to Knowledge (D2K) Lab, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, 110 067, India |
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| Cites_doi | 10.1016/j.imu.2020.100377 10.1016/j.bspc.2023.105195 10.1016/j.procs.2019.05.028 10.1016/j.eswa.2022.119269 10.1016/j.advengsoft.2013.12.007 10.1016/j.compbiomed.2021.104688 10.1016/j.swevo.2020.100794 10.1016/j.asoc.2012.06.021 10.1142/S0219649223500107 10.1016/j.advengsoft.2017.01.004 10.1007/s00521-020-04789-8 10.1016/j.cor.2011.09.026 10.1016/j.energy.2022.125961 10.1016/j.ejca.2021.06.047 10.1016/j.inpa.2019.09.002 10.1016/j.asoc.2011.05.047 10.1109/ICNN.1995.488968 10.1109/72.712155 10.1016/j.compbiomed.2020.103758 10.1371/journal.pone.0278126 10.1007/s00530-021-00776-8 10.1016/j.jcjo.2018.04.019 10.1109/TMI.2004.825627 10.1007/s11042-020-09457-6 10.1007/s11277-023-10255-0 10.1007/s11042-021-11118-1 10.1007/s40998-023-00611-y |
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| Keywords | Convolutional Neural Network Hard Exudates Jaya Optimization Algorithm Nature-Inspired Algorithms Generalization Deep Learning Diabetic Retinopathy |
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| SubjectTerms | Convolutional Neural Network Deep Learning Diabetic Retinopathy Generalization Hard Exudates Jaya Optimization Algorithm Nature-Inspired Algorithms |
| Title | A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy |
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