Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentat...

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Published in:	Frontiers in bioengineering and biotechnology Vol. 11; p. 1286966
Main Authors:	Bhansali, Ashok, Patra, Rajkumar, Abouhawwash, Mohamed, Askar, S. S., Awasthy, Mohan, Rao, K. B. V. Brahma
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media SA 19.12.2023 Frontiers Media S.A
Subjects:	Accuracy adaptive-active counter model Algorithms Bioengineering and Biotechnology Blood vessels Boundary conditions Classification Deep learning Diabetes Diabetes mellitus Diabetic retinopathy gray level co-occurrence matrix Image processing level set method with improved boundary indicator function Long short-term memory Neural networks Optimization Retinopathy Segmentation Visual impairment Wavelet transforms adaptive-active counter model level set method with improved boundary indicator function gray level co-occurrence matrix diabetic retinopathy long short term memory
ISSN:	2296-4185, 2296-4185
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Abstract	Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).
AbstractList	Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE). Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).
Author	Abouhawwash, Mohamed Rao, K. B. V. Brahma Askar, S. S. Patra, Rajkumar Bhansali, Ashok Awasthy, Mohan
AuthorAffiliation	4 Department of Mathematics , Faculty of Science , Mansoura University , Mansoura , Egypt 1 Deptartment of Computer Engineering and Applications , GLA University , Mathura , India 5 Department of Statistics and Operations Research , College of Science , King Saud University , Riyadh , Saudi Arabia 2 Department of Computer Science and Engineering , CMR Technical Campus , Hyderabad , India 6 Department of Engineering and Technology , Bharati Vidyapeeth Deemed to be University , Navi Mumbai , India 7 Department of Computer Science and Engineering , Koneru Lakshmaiah Education Foundation , Guntur , India 3 Department of Computational Mathematics, Science and Engineering (CMSE) , College of Engineering , Michigan State University , East Lansing , MI , United States
AuthorAffiliation_xml	– name: 1 Deptartment of Computer Engineering and Applications , GLA University , Mathura , India – name: 5 Department of Statistics and Operations Research , College of Science , King Saud University , Riyadh , Saudi Arabia – name: 4 Department of Mathematics , Faculty of Science , Mansoura University , Mansoura , Egypt – name: 3 Department of Computational Mathematics, Science and Engineering (CMSE) , College of Engineering , Michigan State University , East Lansing , MI , United States – name: 6 Department of Engineering and Technology , Bharati Vidyapeeth Deemed to be University , Navi Mumbai , India – name: 7 Department of Computer Science and Engineering , Koneru Lakshmaiah Education Foundation , Guntur , India – name: 2 Department of Computer Science and Engineering , CMR Technical Campus , Hyderabad , India
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Keywords	adaptive-active counter model level set method with improved boundary indicator function gray level co-occurrence matrix diabetic retinopathy long short term memory
Language	English
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Snippet	Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by...
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SubjectTerms	Accuracy adaptive-active counter model Algorithms Bioengineering and Biotechnology Blood vessels Boundary conditions Classification Deep learning Diabetes Diabetes mellitus Diabetic retinopathy gray level co-occurrence matrix Image processing level set method with improved boundary indicator function Long short-term memory Neural networks Optimization Retinopathy Segmentation Visual impairment Wavelet transforms
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Title	Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification
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