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 | ||||||||
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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 |