Entropy-based deep neural network training optimization for optical coherence tomography imaging
This paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significantly decreases training epochs. Using a four-class Optical Coherence Tomography (OCT) image dataset encom...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2355760 |
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| _version_ | 1846119936804519936 |
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| author | Karri Karthik Manjunatha Mahadevappa |
| author_facet | Karri Karthik Manjunatha Mahadevappa |
| author_sort | Karri Karthik |
| collection | DOAJ |
| description | This paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significantly decreases training epochs. Using a four-class Optical Coherence Tomography (OCT) image dataset encompassing Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal retina categories, we evaluated twelve architectures. These include general-purpose models (Alexnet, VGG11, VGG13, VGG16, VGG19, ResNet-18, ResNet-34, and ResNet-50) and OCT image-specific models (RetiNet, AOCT-NET, DeepOCT, and Octnet). The proposed technique reduced training epochs ranging from 4.35% to 58.27% for all architectures except Alexnet. Although the overall increase in accuracy ranges from 0.28% to 12.6%, with some architectures experiencing minor improvements, this is seen as acceptable considering the substantial reduction in training time. By achieving higher accuracy with fewer training epochs and eliminating the need for separate validation data, our methodology streamlines early stopping significantly. Statistical evaluations via Shapiro-Wilk and Kruskal-Wallis tests further affirm these results, showcasing the potential of this novel technique for efficient deep learning practices in scenarios constrained by time or computational resources. |
| format | Article |
| id | doaj-art-9cbb1b8daa0c4018a45b7bba09db2f8d |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-9cbb1b8daa0c4018a45b7bba09db2f8d2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2355760Entropy-based deep neural network training optimization for optical coherence tomography imagingKarri Karthik0Manjunatha Mahadevappa1School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, IndiaSchool of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, IndiaThis paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significantly decreases training epochs. Using a four-class Optical Coherence Tomography (OCT) image dataset encompassing Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal retina categories, we evaluated twelve architectures. These include general-purpose models (Alexnet, VGG11, VGG13, VGG16, VGG19, ResNet-18, ResNet-34, and ResNet-50) and OCT image-specific models (RetiNet, AOCT-NET, DeepOCT, and Octnet). The proposed technique reduced training epochs ranging from 4.35% to 58.27% for all architectures except Alexnet. Although the overall increase in accuracy ranges from 0.28% to 12.6%, with some architectures experiencing minor improvements, this is seen as acceptable considering the substantial reduction in training time. By achieving higher accuracy with fewer training epochs and eliminating the need for separate validation data, our methodology streamlines early stopping significantly. Statistical evaluations via Shapiro-Wilk and Kruskal-Wallis tests further affirm these results, showcasing the potential of this novel technique for efficient deep learning practices in scenarios constrained by time or computational resources.https://www.tandfonline.com/doi/10.1080/08839514.2024.2355760 |
| spellingShingle | Karri Karthik Manjunatha Mahadevappa Entropy-based deep neural network training optimization for optical coherence tomography imaging Applied Artificial Intelligence |
| title | Entropy-based deep neural network training optimization for optical coherence tomography imaging |
| title_full | Entropy-based deep neural network training optimization for optical coherence tomography imaging |
| title_fullStr | Entropy-based deep neural network training optimization for optical coherence tomography imaging |
| title_full_unstemmed | Entropy-based deep neural network training optimization for optical coherence tomography imaging |
| title_short | Entropy-based deep neural network training optimization for optical coherence tomography imaging |
| title_sort | entropy based deep neural network training optimization for optical coherence tomography imaging |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2355760 |
| work_keys_str_mv | AT karrikarthik entropybaseddeepneuralnetworktrainingoptimizationforopticalcoherencetomographyimaging AT manjunathamahadevappa entropybaseddeepneuralnetworktrainingoptimizationforopticalcoherencetomographyimaging |