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Sparse Autoencoder Based Microaneurysm Detection

EasyChair Preprint no. 9348

5 pagesDate: November 22, 2022


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 autoencoder (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 dataset DIARETDB1. The result achieved a better accuracy compared to other methods.

Keyphrases: Micro aneurysm (MA), Stacked Sparse Autoencoder, Support Vector Machine, unsupervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {P S Bindhya and R Chitra and V S Bibinraj},
  title = {Sparse Autoencoder Based Microaneurysm Detection},
  howpublished = {EasyChair Preprint no. 9348},

  year = {EasyChair, 2022}}
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