Early diagnosis of diabetic retinopathy using unsupervised learning

Padmapriya, M.; Pasupathy, S.; Punitha, V.

doi:10.1007/s00500-023-08418-z

Soft Comput

2023

DOI: 10.1007/s00500-023-08418-z

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Early diagnosis of diabetic retinopathy using unsupervised learning

M. Padmapriya

S. Pasupathy

V. Punitha³

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2024

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Cited by 1 publication

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EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

Prethija,

Katiravan

2024

MBE

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<abstract> <p>Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively.</p> </abstract>

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EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

Prethija,

Katiravan

2024

MBE

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Early diagnosis of diabetic retinopathy using unsupervised learning

Cited by 1 publication

References 49 publications

EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

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