Earlier detection of rop and rop sub-classification using TA-CHD-DNN and UD-TFCM from ultrasound digital B-Scan images
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Full Text |
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Author |
K. R. N. Aswini and S. Vijayaraghavan
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e-ISSN |
1819-6608 |
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On Pages
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2112-2120
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Volume No. |
18
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Issue No. |
18
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Issue Date |
November 30, 2023
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DOI |
https://doi.org/10.59018/0923259
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Keywords |
retinal blood vessel, segmentation, choroidal neovascularization (CNV), diabetic macular edema (DME), uniform distribution (UD), trapezoidal fuzzy C means (TFCM), twin active- cosine-hausdorff distance- deep neural network (TA-CHD-DNN).
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Abstract
The growth of the retinal blood vessels of the baby begins at 16 weeks and even after the birth of the baby, they
don’t grow completely. For the effective analysis of Retinopathy of Prematurity (ROP), diverse methodologies are
employed. Owing to the insufficient growth of tangled patterns, greater retinal layer thickness, and the presence of
hyperreflective material, the majority of the traditional methodologies were unsuccessful in classifying the Choroidal
Neovascularization (CNV), Diabetic Macular Edema (DME), and DRUSEN stages of ROP. Thus, for the classification of
different stages of the ROP, an effective Twin Active- Cosine-Hausdorff Distance-Deep Neural Network (TA-CHD-DNN)
is proposed in this paper. Chiefly, the image obtained from the retinal image dataset is pre-processed and fed into TA-
CHD-DNN. After the extraction of critical features, the segmentation of the retinal layer, choroid layer, and vascular layer
occurs in TA-CHD- DNN. The classifier is trained to make decisions for the classification of diverse stages of ROP
centered on the extracted features. The normal or abnormal stages of CNV, DME, and DRUSEN are classified by the
classifier during testing. By utilizing the Uniformly Distributed-Trapezoidal Fuzzy C Means (UD-TFCM) technique, the
decision regarding the severity stage of CNV, DME, and DRUSEN is made in the abnormal stage. Thus, centered on the
experimental outcomes, the proposed system performance will be analogized to the conventional techniques to confirm the
proposed system’s efficiency.
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