Diabetic Retinopathy Detection Using Local Extrema Quantized Haralick Features with Long Short-Term Memory Network
| Autor: | Salisu Ibrahim, Mohammed S. Anwar, Abubakar M. Ashir, Abdullahi Abdu Ibrahim, Mohammed Abdulghani |
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| Přispěvatelé: | Ibrahim, Abdullahi Abdu |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
Computer science Fundus image R895-920 Early detection Network 02 engineering and technology 030218 nuclear medicine & medical imaging Medical physics. Medical radiology. Nuclear medicine 03 medical and health sciences Long short term memory DR 0302 clinical medicine Medical technology 0202 electrical engineering electronic engineering information engineering medicine Long Short-Term Memory Radiology Nuclear Medicine and imaging R855-855.5 Total blindness business.industry Probabilistic logic Pattern recognition Diabetic retinopathy medicine.disease Maxima and minima Feature (computer vision) 020201 artificial intelligence & image processing Artificial intelligence LSTM business Research Article |
| Zdroj: | International Journal of Biomedical Imaging International Journal of Biomedical Imaging, Vol 2021 (2021) |
| ISSN: | 1687-4188 |
| DOI: | 10.1155/2021/6618666 |
| Popis: | WOS:000644175800001 PubMed: 33953736 Diabetic retinopathy is one of the leading diseases affecting eyes. Lack of early detection and treatment can lead to total blindness of the diseased eyes. Recently, numerous researchers have attempted producing automatic diabetic retinopathy detection techniques to supplement diagnosis and early treatment of diabetic retinopathy symptoms. In this manuscript, a new approach has been proposed. The proposed approach utilizes the feature extracted from the fundus image using a local extrema information with quantized Haralick features. The quantized features encode not only the textural Haralick features but also exploit the multiresolution information of numerous symptoms in diabetic retinopathy. Long Short-Term Memory network together with local extrema pattern provides a probabilistic approach to analyze each segment of the image with higher precision which helps to suppress false positive occurrences. The proposed approach analyzes the retina vasculature and hard-exudate symptoms of diabetic retinopathy on two different public datasets. The experimental results evaluated using performance matrices such as specificity, accuracy, and sensitivity reveal promising indices. Similarly, comparison with the related state-of-the-art researches highlights the validity of the proposed method. The proposed approach performs better than most of the researches used for comparison. |
| Databáze: | OpenAIRE |
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