0000000001297685

AUTHOR

Ibrahim Sadek

showing 2 related works from this author

Discrimination of retinal images containing bright lesions using sparse coded features and SVM

2015

Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retina…

MaleDatabases Factualgenetic structuresFeature extractionHealth Informatics02 engineering and technologyDrusen[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Retina030218 nuclear medicine & medical imaging03 medical and health scienceschemistry.chemical_compound0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineImage Processing Computer-AssistedHumansComputer visionRetinaDiabetic RetinopathyContextual image classificationbusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]RetinalDiabetic retinopathymedicine.diseaseComputer Science ApplicationsSupport vector machinemedicine.anatomical_structurechemistry020201 artificial intelligence & image processingFemaleArtificial intelligenceNeural codingbusiness
researchProduct

Automatic Classification of Bright Retinal Lesions via Deep Network Features

2018

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches…

FOS: Computer and information sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]genetic structuresComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognition[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
researchProduct