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Table 3 Sensitivities of the different methods in Retinopathy Online Challenge dataset at the various FP/image rates

From: Microaneurysm detection in fundus images using a two-step convolutional neural network

Free-response receiver operating characteristic results on Retinopathy Online Challenge dataset at average number of False positives per image
FPs/img
Sensitivity
Method 1/8 1/4 1/2 1 2 4 8 Classification method
 Proposed method 0.047 0.173 0.351 0.552 0.613 0.722 0.769 CNN
 Dashtbozorg [38] 0.435 0.443 0.454 0.476 0.481 0.495 0.506 RUSBoost
 Chudzik [29] 0.142 0.201 0.250 0.325 0.365 0.390 0.409 CNN
 Budak [28] 0.039 0.061 0.121 0.220 0.338 0.372 0.394 DCNN
 Javidi [40] 0.130 0.147 0.209 0.287 0.319 0.353 0.383 Discriminative dictionary learning
 Wu’s [39] 0.037 0.056 0.103 0.206 0.295 0.339 0.376 KNN
 Valladolid [42]* 0.190 0.216 0.254 0.300 0.364 0.411 0.519 GMM
 Waikato group [41]* 0.055 0.111 0.184 0.213 0.251 0.300 0.329 Bayesian
 Latim [25]* 0.166 0.230 0.318 0.385 0.434 0.534 0.598 Thresholding
 OkMedical [10]* 0.198 0.265 0.315 0.356 0.394 0.466 0.501 Dynamic thresholding
 Fujita Lab [43]* 0.181 0.224 0.259 0.289 0.347 0.402 0.466 ANN
  1. The quantity given in italic form in each FPs/Img column represents the best result
  2. * Indicate papers which use the full original dataset and others which use the cross-validation technique