A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine
The role of diabetes mellitus in deteriorating the visual health of diabetic subjects has been affirmed precisely. The study of morphological features near the macular region is the most common method of investigating the impairment rate. The general mode of diagnosis carried out by manual inspectio...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | AIMS Electronics and Electrical Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/electreng.2023001?viewType=HTML |
| _version_ | 1797824291084959744 |
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| author | Manisha Bangar Prachi Chaudhary |
| author_facet | Manisha Bangar Prachi Chaudhary |
| author_sort | Manisha Bangar |
| collection | DOAJ |
| description | The role of diabetes mellitus in deteriorating the visual health of diabetic subjects has been affirmed precisely. The study of morphological features near the macular region is the most common method of investigating the impairment rate. The general mode of diagnosis carried out by manual inspection of fundus imaging, is less effective and slow. The goal of this study is to provide a novel approach to classify optical coherence tomography images effectively and efficiently. discrete wavelet transform and fast fourier transform are utilized to extract features, and a kernel-based support vector machine is used as classifier. To improve image contrast, histogram equalization is performed. Segmentation of the enhanced images is performed using k-means clustering. The hybrid feature extraction technique comprising the discrete wavelet transform and fast fourier transform renders novelty to the study. In terms of classification accuracy, the system's efficiency is compared to that of earlier available techniques. The suggested approach attained an overall accuracy of 96.46 % over publicly available datasets. The classifier accuracy of the system is found to be better than the performance of the discrete wavelet transform with self organizing maps and support vector machines with a linear kernel. |
| first_indexed | 2024-03-13T10:36:47Z |
| format | Article |
| id | doaj.art-2a5e477e4545499482d8f0923e9a5613 |
| institution | Directory Open Access Journal |
| issn | 2578-1588 |
| language | English |
| last_indexed | 2024-03-13T10:36:47Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj.art-2a5e477e4545499482d8f0923e9a56132023-05-18T05:39:20ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882023-01-017111310.3934/electreng.2023001A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machineManisha Bangar0Prachi Chaudhary1Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, IndiaDepartment of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, IndiaThe role of diabetes mellitus in deteriorating the visual health of diabetic subjects has been affirmed precisely. The study of morphological features near the macular region is the most common method of investigating the impairment rate. The general mode of diagnosis carried out by manual inspection of fundus imaging, is less effective and slow. The goal of this study is to provide a novel approach to classify optical coherence tomography images effectively and efficiently. discrete wavelet transform and fast fourier transform are utilized to extract features, and a kernel-based support vector machine is used as classifier. To improve image contrast, histogram equalization is performed. Segmentation of the enhanced images is performed using k-means clustering. The hybrid feature extraction technique comprising the discrete wavelet transform and fast fourier transform renders novelty to the study. In terms of classification accuracy, the system's efficiency is compared to that of earlier available techniques. The suggested approach attained an overall accuracy of 96.46 % over publicly available datasets. The classifier accuracy of the system is found to be better than the performance of the discrete wavelet transform with self organizing maps and support vector machines with a linear kernel.https://www.aimspress.com/article/doi/10.3934/electreng.2023001?viewType=HTMLdiabetic macular edemak-means clusteringdiscrete wavelet transformoptical coherence tomographyk-svmhistogram |
| spellingShingle | Manisha Bangar Prachi Chaudhary A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine AIMS Electronics and Electrical Engineering diabetic macular edema k-means clustering discrete wavelet transform optical coherence tomography k-svm histogram |
| title | A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| title_full | A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| title_fullStr | A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| title_full_unstemmed | A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| title_short | A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| title_sort | novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine |
| topic | diabetic macular edema k-means clustering discrete wavelet transform optical coherence tomography k-svm histogram |
| url | https://www.aimspress.com/article/doi/10.3934/electreng.2023001?viewType=HTML |
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