Sparse auto-encoder based micro-aneurysm detection

Microaneurysms (MAs) are the early noticeable lesions in retina, their detection plays a crucial role in the diagnosis of diabetic retinopathy. Here the discriminative features are automatically learned in an unsupervised manner. The stacked sparse auto-encoder (SSAE) is effective at learning high-l...

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Hauptverfasser:	Bindhya, P. S., Chitra, R., Raj, V. S. Bibin
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Sprache:	eng
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creator	Bindhya, P. S. Chitra, R. Raj, V. S. Bibin
description	Microaneurysms (MAs) are the early noticeable lesions in retina, their detection plays a crucial role in the diagnosis of diabetic retinopathy. Here the discriminative features are automatically learned in an unsupervised manner. The stacked sparse auto-encoder (SSAE) is effective at learning high-level features from overlapping image patches during training. Using 10-fold cross-validation and fine-tuning yield an improved F-measure of 97.3% and an average area under the ROC curve (AUC) 96.7% obtained. Experimental validation is performed, both quantitative and qualitative, in the public datasetDIARETDB1. The result achieved a better accuracy compared to other methods.
doi_str_mv	10.1063/5.0170568
format	Conference Proceeding
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title	Sparse auto-encoder based micro-aneurysm detection
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