Classification of noiseless corneal image using capsule networks

Classifying a particular image from a data set is a complex work for any image analyst. Generally, the output of medical image scan gives numerous images for analysis. In that, the image analyst has to manually predict a better noiseless image for computer-assisted image process program. Manual verification of all the output images from the scan device consumes a lot of time in predicting the abnormality of a patient. The proposed capsule network for noiseless image algorithm assists the image analyst by classifying the noiseless image from the data set for further computer-assisted image enhancement or segmentation program. The proposed algorithm performance is evaluated and compared with the existing algorithms in terms of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.