Academic Journal
Retinopathy grading with deep learning and wavelet hyper-analytic activations.
| Title: | Retinopathy grading with deep learning and wavelet hyper-analytic activations. |
|---|---|
| Authors: | Chandrasekaran, Raja, Loganathan, Balaji |
| Source: | Visual Computer; Jul2023, Vol. 39 Issue 7, p2741-2756, 16p |
| Subject Terms: | Deep learning, Convolutional neural networks, Digital preservation, Diabetic retinopathy |
| Abstract: | Recent developments reveal the prominence of Diabetic Retinopathy (DR) grading. In the past few decades, Wavelet-based DR classification has shown successful impacts and the Deep Learning models, like Convolutional Neural Networks (CNN's), have evolved in offering the highest prediction accuracy. In this work, the features of the input image are enhanced with the integration of Multi-Resolution Analysis (MRA) and a CNN framework without costing more convolution filters. The bottleneck with conventional activation functions, used in CNN's, is the nullification of the feature maps that are negative in value. In this work, a novel Hyper-analytic Wavelet (HW) phase activation function is formulated with unique characteristics for the wavelet sub-bands. Instead of dismissal, the function transforms these negative coefficients that correspond to significant edge feature maps. The hyper-analytic wavelet phase forms the imaginary part of the complex activation. And the hyper-parameter of the activation function is selected such that the corresponding magnitude spectrum produces monotonic and effective activations. The performance of 3 CNN models (1 custom, shallow CNN, ResNet with Soft attention, Alex Net for DR) with spatial–Wavelet quilts is better. With the spatial–Wavelet quilts, the Alex Net for DR has an improvement with an 11% of accuracy level (from 87 to 98%). The highest accuracy level of 98% and the highest Sensitivity of 99% are attained through Modified Alex Net for DR. The proposal also illustrates the visualization of the negative edge preservation with assumed image patches. From this study, the researcher infers that models with spatial–Wavelet quilts, with the hyper-analytic activations, have better generalization ability. And the visualization of heat maps provides evidence of better learning of the feature maps from the wavelet sub-bands. [ABSTRACT FROM AUTHOR] |
| Copyright of Visual Computer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s00371-022-02489-z Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 164610611 RelevancyScore: 932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 931.961059570313 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Retinopathy grading with deep learning and wavelet hyper-analytic activations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chandrasekaran%2C+Raja%22">Chandrasekaran, Raja</searchLink><br /><searchLink fieldCode="AR" term="%22Loganathan%2C+Balaji%22">Loganathan, Balaji</searchLink> – Name: TitleSource Label: Source Group: Src Data: Visual Computer; Jul2023, Vol. 39 Issue 7, p2741-2756, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+preservation%22">Digital preservation</searchLink><br /><searchLink fieldCode="DE" term="%22Diabetic+retinopathy%22">Diabetic retinopathy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent developments reveal the prominence of Diabetic Retinopathy (DR) grading. In the past few decades, Wavelet-based DR classification has shown successful impacts and the Deep Learning models, like Convolutional Neural Networks (CNN's), have evolved in offering the highest prediction accuracy. In this work, the features of the input image are enhanced with the integration of Multi-Resolution Analysis (MRA) and a CNN framework without costing more convolution filters. The bottleneck with conventional activation functions, used in CNN's, is the nullification of the feature maps that are negative in value. In this work, a novel Hyper-analytic Wavelet (HW) phase activation function is formulated with unique characteristics for the wavelet sub-bands. Instead of dismissal, the function transforms these negative coefficients that correspond to significant edge feature maps. The hyper-analytic wavelet phase forms the imaginary part of the complex activation. And the hyper-parameter of the activation function is selected such that the corresponding magnitude spectrum produces monotonic and effective activations. The performance of 3 CNN models (1 custom, shallow CNN, ResNet with Soft attention, Alex Net for DR) with spatial–Wavelet quilts is better. With the spatial–Wavelet quilts, the Alex Net for DR has an improvement with an 11% of accuracy level (from 87 to 98%). The highest accuracy level of 98% and the highest Sensitivity of 99% are attained through Modified Alex Net for DR. The proposal also illustrates the visualization of the negative edge preservation with assumed image patches. From this study, the researcher infers that models with spatial–Wavelet quilts, with the hyper-analytic activations, have better generalization ability. And the visualization of heat maps provides evidence of better learning of the feature maps from the wavelet sub-bands. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Visual Computer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=164610611 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00371-022-02489-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2741 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Digital preservation Type: general – SubjectFull: Diabetic retinopathy Type: general Titles: – TitleFull: Retinopathy grading with deep learning and wavelet hyper-analytic activations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chandrasekaran, Raja – PersonEntity: Name: NameFull: Loganathan, Balaji IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 01782789 Numbering: – Type: volume Value: 39 – Type: issue Value: 7 Titles: – TitleFull: Visual Computer Type: main |
| ResultId | 1 |