Authors:
Chaymaa Lahmar
1
and
Ali Idri
1
;
2
Affiliations:
1
Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco
;
2
Mohammed VI Polytechnic University Benguerir, Morocco
Keyword(s):
Diabetic Retinopathy, Homogeneous Ensembles, Machine Learning, Deep Learning, Feature Selection, Fundus Images.
Abstract:
Diabetic Retinopathy (DR) is the most frequent cause of blindness and visual impairment among working-age adults in the world. Machine learning (ML) and deep learning (DL) techniques are playing an important role in the early detection of DR. This paper proposes a new homogeneous ensemble approach constructed using a set of hybrid architectures, as base learners, and two combination rules (hard and weighted voting) for referable DR detection using fundus images over the Kaggle DR, APTOS and Messidor-2 datasets. The hybrid architectures are created using seven deep feature extractors (DenseNet201, InceptionResNetV2, MobileNetV2, InceptionV3, VGG16, VGG19, and ResNet50), six dimensionality reduction techniques (Principal component analysis, Select from model feature selection, Recursive feature elimination with cross-validation, Factor analysis, Chi-Square test, and Low variance filter), and k-nearest neighbors algorithm (KNN) for classification. The results showed the importance of th
e proposed approach considering that it outperformed its base learners, and achieved an accuracy value of 92.47% for the Kaggle DR dataset, 89.59% for the APTOS dataset, and 82.03% for the Messidor-2 dataset. The experimental results demonstrated that the proposed approach is impactful for the detection of referable DR, and thus represents a promising tool to assist ophthalmologists in the diagnosis of DR.
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