Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset
Retinal disorders like diabetic retinopathy pose a significant threat to global vision. Early diagnosis is crucial, and fundus images provide vital insights into retinal conditions, focusing on blood vessel characteristics. Manual retinal vessel segmentation, though precise, is time-consuming and de...
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Bilijipub publisher
2024-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_193336_ede00a77c0fed33e518d6183ddc88c18.pdf |
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| author | Asadi Srinivasulu Clement Varaprasad Karu G Sreenivasulu Gayathri R |
| author_facet | Asadi Srinivasulu Clement Varaprasad Karu G Sreenivasulu Gayathri R |
| author_sort | Asadi Srinivasulu |
| collection | DOAJ |
| description | Retinal disorders like diabetic retinopathy pose a significant threat to global vision. Early diagnosis is crucial, and fundus images provide vital insights into retinal conditions, focusing on blood vessel characteristics. Manual retinal vessel segmentation, though precise, is time-consuming and dependent on skilled professionals. Addressing this, an automatic and efficient retinal vessel segmentation method is urgently needed, utilizing computer vision techniques. Existing approaches include machine learning, filtering-based, and model-based methods. Our research aims to evaluate automated segmentation and classification techniques for diabetic retinopathy and glaucoma using diverse retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate, true negative rate, positive predictive value, negative predictive value, false discovery rate, Matthews's correlation coefficient, and accuracy. The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach. |
| format | Article |
| id | doaj-art-b976229db8f84bdfb38c9c9fd3ac8837 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-b976229db8f84bdfb38c9c9fd3ac88372025-02-12T08:47:46ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-03-0100301455510.22034/aeis.2024.430000.1152193336Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR DatasetAsadi Srinivasulu0Clement Varaprasad Karu1G Sreenivasulu2Gayathri R3Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, New South Wales, 2308, AustraliaFaculty of Engineering, Sohar University, Sohar, 311, OmanDepatment.of Biotechnology, Prathyusha Engineering College, Thiruvallur, Tamil Nadu, 602025, IndiaDepartment of Research and Development, Sree Dattha Institute of Engineering and Science, Telangana, 501510, IndiaRetinal disorders like diabetic retinopathy pose a significant threat to global vision. Early diagnosis is crucial, and fundus images provide vital insights into retinal conditions, focusing on blood vessel characteristics. Manual retinal vessel segmentation, though precise, is time-consuming and dependent on skilled professionals. Addressing this, an automatic and efficient retinal vessel segmentation method is urgently needed, utilizing computer vision techniques. Existing approaches include machine learning, filtering-based, and model-based methods. Our research aims to evaluate automated segmentation and classification techniques for diabetic retinopathy and glaucoma using diverse retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate, true negative rate, positive predictive value, negative predictive value, false discovery rate, Matthews's correlation coefficient, and accuracy. The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.https://aeis.bilijipub.com/article_193336_ede00a77c0fed33e518d6183ddc88c18.pdfdiabetic retinopathyglaucomaretinal vessel segmentationdisease classificationfundus images |
| spellingShingle | Asadi Srinivasulu Clement Varaprasad Karu G Sreenivasulu Gayathri R Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset Advances in Engineering and Intelligence Systems diabetic retinopathy glaucoma retinal vessel segmentation disease classification fundus images |
| title | Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset |
| title_full | Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset |
| title_fullStr | Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset |
| title_full_unstemmed | Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset |
| title_short | Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset |
| title_sort | automatic detection and classification of eye diseases from retinal images using deep learning a comprehensive research on the odir dataset |
| topic | diabetic retinopathy glaucoma retinal vessel segmentation disease classification fundus images |
| url | https://aeis.bilijipub.com/article_193336_ede00a77c0fed33e518d6183ddc88c18.pdf |
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