A hybrid BPSO‐SVM for feature selection and classification of ocular health
Abstract Glaucoma and diabetic retinopathy are the most common eye diseases and the leading cause of blindness around the world. The prime objective of this study is to devise and develop an experimental computer‐aided diagnosis system to provide an efficient way for assisting the ophthalmologist in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-02-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12047 |
| _version_ | 1811251929782681600 |
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| author | B. Keerthiveena S. Esakkirajan Badri Narayan Subudhi T. Veerakumar |
| author_facet | B. Keerthiveena S. Esakkirajan Badri Narayan Subudhi T. Veerakumar |
| author_sort | B. Keerthiveena |
| collection | DOAJ |
| description | Abstract Glaucoma and diabetic retinopathy are the most common eye diseases and the leading cause of blindness around the world. The prime objective of this study is to devise and develop an experimental computer‐aided diagnosis system to provide an efficient way for assisting the ophthalmologist in early detection of ocular diseases such as glaucoma and diabetic retinopathy. The proposed technique follows three stages: Pre‐processing, feature selection and classification. Initially, the fundus image is pre‐processed to extract the green channel image, and the obtained green channel image is further enhanced using contrast limited adaptive histogram equalisation technique. Three different kinds of features: Clinical features, transform domain features and structural features are utilised to extract the relevant information from the enhanced fundus images. To avoid redundant information, an improved feature selection mechanism is used to select the optimum set of features from the extracted features. Subsequently, the selected features are used to train the support vector machine classifier for the classification of the retinal diseases with 10‐fold cross‐validation. The performance of the proposed method is assessed using eight different quantitative evaluation measures. The experimental results demonstrate the effectiveness of the proposed work over prior works for the early detection of ocular diseases. |
| first_indexed | 2024-04-12T16:27:20Z |
| format | Article |
| id | doaj.art-4c696282ec0c403fb6c7d8b12a000041 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-12T16:27:20Z |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-4c696282ec0c403fb6c7d8b12a0000412022-12-22T03:25:19ZengWileyIET Image Processing1751-96591751-96672021-02-0115254255510.1049/ipr2.12047A hybrid BPSO‐SVM for feature selection and classification of ocular healthB. Keerthiveena0S. Esakkirajan1Badri Narayan Subudhi2T. Veerakumar3Department of Instrumentation and Control systems Engineering PSG College of Technology Coimbatore Tamil Nadu 641004 IndiaDepartment of Instrumentation and Control systems Engineering PSG College of Technology Coimbatore Tamil Nadu 641004 IndiaDepartment of Electrical Engineering Indian Institute of Technology Jammu Jammu Jammu & Kashmir 181221 IndiaDepartment of Electronics and Communication Engineering National Institute of Technology Goa Goa Goa 403401 IndiaAbstract Glaucoma and diabetic retinopathy are the most common eye diseases and the leading cause of blindness around the world. The prime objective of this study is to devise and develop an experimental computer‐aided diagnosis system to provide an efficient way for assisting the ophthalmologist in early detection of ocular diseases such as glaucoma and diabetic retinopathy. The proposed technique follows three stages: Pre‐processing, feature selection and classification. Initially, the fundus image is pre‐processed to extract the green channel image, and the obtained green channel image is further enhanced using contrast limited adaptive histogram equalisation technique. Three different kinds of features: Clinical features, transform domain features and structural features are utilised to extract the relevant information from the enhanced fundus images. To avoid redundant information, an improved feature selection mechanism is used to select the optimum set of features from the extracted features. Subsequently, the selected features are used to train the support vector machine classifier for the classification of the retinal diseases with 10‐fold cross‐validation. The performance of the proposed method is assessed using eight different quantitative evaluation measures. The experimental results demonstrate the effectiveness of the proposed work over prior works for the early detection of ocular diseases.https://doi.org/10.1049/ipr2.12047Optical, image and video signal processingBiomedical measurement and imagingComputer vision and image processing techniquesOptical and laser radiation (medical uses)Patient diagnostic methods and instrumentationBiology and medical computing |
| spellingShingle | B. Keerthiveena S. Esakkirajan Badri Narayan Subudhi T. Veerakumar A hybrid BPSO‐SVM for feature selection and classification of ocular health IET Image Processing Optical, image and video signal processing Biomedical measurement and imaging Computer vision and image processing techniques Optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing |
| title | A hybrid BPSO‐SVM for feature selection and classification of ocular health |
| title_full | A hybrid BPSO‐SVM for feature selection and classification of ocular health |
| title_fullStr | A hybrid BPSO‐SVM for feature selection and classification of ocular health |
| title_full_unstemmed | A hybrid BPSO‐SVM for feature selection and classification of ocular health |
| title_short | A hybrid BPSO‐SVM for feature selection and classification of ocular health |
| title_sort | hybrid bpso svm for feature selection and classification of ocular health |
| topic | Optical, image and video signal processing Biomedical measurement and imaging Computer vision and image processing techniques Optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing |
| url | https://doi.org/10.1049/ipr2.12047 |
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