AHO-MLCNN: archerfish hunting optimisation based modified lightweight CNN for diabetic retinopathy detection.
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| Title: | AHO-MLCNN: archerfish hunting optimisation based modified lightweight CNN for diabetic retinopathy detection. |
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| Authors: | Desika Vinayaki, V.1 (AUTHOR) vdesika.id@gmail.com, Kalaiselvi, R.1 (AUTHOR) |
| Source: | Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation. 2023, Vol. 11 Issue 5, p1937-1946. 10p. |
| Subjects: | Diabetic retinopathy, Hyperglycemia, Retinal diseases, Hunting, Disease progression |
| Abstract: | This paper presents a novel automated model for the detection of diabetic retinopathy (DR), a common retinal disease caused by high blood sugar levels. Early detection of DR is crucial in preventing severe complications. The proposed model focuses on effectively classifying retinopathy and non-retinopathy cases using two fundus image datasets: DIARETDB0 and IDRiD. The DR detection module consists of two phases: preprocessing and classification. In the preprocessing phase, tasks such as resizing, normalization, and denoising are performed to enhance the accuracy of the classifier. The preprocessed fundus images are then fed into the proposed modified residual block lightweight CNN-based archerfish hunting optimizer (MRLCNN-AHO) approach, which accurately detects and classifies normal and abnormal cases. The experimental results demonstrate the efficiency of the proposed MRLCNN-AHO approach. It achieves an accuracy rate of approximately 97.8% and 97.5% for DIARETDB0 and IDRiD datasets, respectively. These results are compared with various existing methods, validating the effectiveness of the proposed approach. Automated DR detection models like the one proposed in this paper contribute to reducing processing time, cost, and effort associated with manual diagnosis. Early identification of mild-stage DR can significantly improve patient outcomes by enabling timely interventions and preventing the progression of the disease. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 171107188 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: AHO-MLCNN: archerfish hunting optimisation based modified lightweight CNN for diabetic retinopathy detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Desika+Vinayaki%2C+V%2E%22">Desika Vinayaki, V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vdesika.id@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kalaiselvi%2C+R%2E%22">Kalaiselvi, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Methods+in+Biomechanics+%26+Biomedical+Engineering%3A+Imaging+%26+Visualisation%22">Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation</searchLink>. 2023, Vol. 11 Issue 5, p1937-1946. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Diabetic+retinopathy%22">Diabetic retinopathy</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperglycemia%22">Hyperglycemia</searchLink><br /><searchLink fieldCode="DE" term="%22Retinal+diseases%22">Retinal diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Hunting%22">Hunting</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+progression%22">Disease progression</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a novel automated model for the detection of diabetic retinopathy (DR), a common retinal disease caused by high blood sugar levels. Early detection of DR is crucial in preventing severe complications. The proposed model focuses on effectively classifying retinopathy and non-retinopathy cases using two fundus image datasets: DIARETDB0 and IDRiD. The DR detection module consists of two phases: preprocessing and classification. In the preprocessing phase, tasks such as resizing, normalization, and denoising are performed to enhance the accuracy of the classifier. The preprocessed fundus images are then fed into the proposed modified residual block lightweight CNN-based archerfish hunting optimizer (MRLCNN-AHO) approach, which accurately detects and classifies normal and abnormal cases. The experimental results demonstrate the efficiency of the proposed MRLCNN-AHO approach. It achieves an accuracy rate of approximately 97.8% and 97.5% for DIARETDB0 and IDRiD datasets, respectively. These results are compared with various existing methods, validating the effectiveness of the proposed approach. Automated DR detection models like the one proposed in this paper contribute to reducing processing time, cost, and effort associated with manual diagnosis. Early identification of mild-stage DR can significantly improve patient outcomes by enabling timely interventions and preventing the progression of the disease. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/21681163.2023.2203262 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1937 Subjects: – SubjectFull: Diabetic retinopathy Type: general – SubjectFull: Hyperglycemia Type: general – SubjectFull: Retinal diseases Type: general – SubjectFull: Hunting Type: general – SubjectFull: Disease progression Type: general Titles: – TitleFull: AHO-MLCNN: archerfish hunting optimisation based modified lightweight CNN for diabetic retinopathy detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Desika Vinayaki, V. – PersonEntity: Name: NameFull: Kalaiselvi, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 21681163 Numbering: – Type: volume Value: 11 – Type: issue Value: 5 Titles: – TitleFull: Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation Type: main |
| ResultId | 1 |
