Open AccessAccurate leukocoria predictor based on deep VGG‐net CNN technique
Author(s) -
Subrahmanyeswara Rao Boyina
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6656
Subject(s) - artificial intelligence , computer science , convolutional neural network , classifier (uml) , pattern recognition (psychology) , feature extraction , deep learning , computer vision , segmentation , object detection , contextual image classification , image (mathematics)
The most important part of digital image analysis is object classification. Nowadays, deep learning makes an enormous achievement in computer vision problems. So there has been a lot of interests in applying features learned by convolutional neural networks (CNNs) on general image recognition to more tasks such as object detection, segmentation and face recognition. Leukocoria detection is one of the serious challenges in infant retinal treatment. Leukocoria is represented as an abnormal white reflection appearing in the eyes of an infant suffering from retinoblastoma. This research proposes a deep Visual Geometry Group‐net CNN classifier for automatic detection of leukocoria. The proposed classifier comprises pre‐processing, feature extraction and classification. The deep CNN classifier contains convolution layer, pooling layer and fully connected layer with weights are developed on each image. Experimental results based on several eye images consist of ordinary and leukocoric from flicker, and it demonstrates that the proposed classifier provides better results with the accuracy of 98.5% and the error rate is below 2% which exceeds the current results.