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Classification of images based on small local features: a case applied to microaneurysms in fundus retina images

Classification of images based on small local features: a case applied to microaneurysms in... Abstract.Convolutional neural networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting referable diabetic retinopathy. Having a size of <1% of the total image, microaneurysms are early lesions in diabetic retinopathy that are difficult to classify. A model that includes two CNNs with different input image sizes, 60×60 and 420×420  pixels, was developed. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity >91%, a specificity >93%, and an area under the receiver operating characteristics curve >93%. Furthermore, by combining these trained models, there was a reduction of false positives for complete images by about 50% and a sensitivity of 96% when tested against the DiaRetDB1 dataset. In addition, a powerful image preprocessing procedure was implemented, improving not only images for annotations, but also decreasing the number of epochs during training. Finally, a feedback method was developed increasing the accuracy of the CNN 420×420  pixel input model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Classification of images based on small local features: a case applied to microaneurysms in fundus retina images

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References (12)

Publisher
SPIE
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.4.4.041309
Publisher site
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Abstract

Abstract.Convolutional neural networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting referable diabetic retinopathy. Having a size of <1% of the total image, microaneurysms are early lesions in diabetic retinopathy that are difficult to classify. A model that includes two CNNs with different input image sizes, 60×60 and 420×420  pixels, was developed. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity >91%, a specificity >93%, and an area under the receiver operating characteristics curve >93%. Furthermore, by combining these trained models, there was a reduction of false positives for complete images by about 50% and a sensitivity of 96% when tested against the DiaRetDB1 dataset. In addition, a powerful image preprocessing procedure was implemented, improving not only images for annotations, but also decreasing the number of epochs during training. Finally, a feedback method was developed increasing the accuracy of the CNN 420×420  pixel input model.

Journal

Journal of Medical Imaging – SPIE

Published: Oct 1, 2017

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