Rapid diagnosis increases the chance of a patient being cured of symptoms. This applies especially to diabetic diseases where there is a high risk of diabetic retinopathy, which will lead to blindness if not treated promptly. Artificial intelligent techniques are proposed to diagnose diabetic retinopathy. In this paper, we recognize diabetic retinopathy from retinal images using meta-learning Convolutional Neural Networks (CNNs). Before training state-of-the-art CNNs, data resampling methods were proposed to select training and validation sets, and then the CNNs were trained on the selected training data. The simple data augmentation techniques were applied when training the CNNs to increase the training data pattern. We compared two ensemble learning methods: meta-learner and unweighted average, to show that the ensemble methods always performed better than when using a single CNN. The results showed that training the CNN model with the random data method outperformed other data resampling methods. However, data augmentation techniques did not present an outstanding result on diabetic retinopathy. In conclusion, the ensemble learning method using the meta-learner method resulted in the best accuracy when compared with unweighted average method. The proposed meta-learner CNNs achieved an accuracy of 86.32%.
Effective Data Resampling and Meta-learning Convolutional Neural Networks for Diabetic Retinopathy recognition
Effective Data Resampling and Meta-learning Convolutional Neural Networks for Diabetic Retinopathy recognition
Rapid diagnosis increases the chance of a patient being cured of symptoms. This applies especially to diabetic diseases where there is a high risk of diabetic retinopathy, which will lead to blindness if not treated promptly. Artificial intelligent techniques are proposed to diagnose diabetic retinopathy. In this paper, we recognize diabetic retinopathy from retinal images using meta-learning Convolutional Neural Networks (CNNs). Before training state-of-the-art CNNs, data resampling methods were proposed to select training and validation sets, and then the CNNs were trained on the selected training data. The simple data augmentation techniques were applied when training the CNNs to increase the training data pattern. We compared two ensemble learning methods: meta-learner and unweighted average, to show that the ensemble methods always performed better than when using a single CNN. The results showed that training the CNN model with the random data method outperformed other data resampling methods. However, data augmentation techniques did not present an outstanding result on diabetic retinopathy. In conclusion, the ensemble learning method using the meta-learner method resulted in the best accuracy when compared with unweighted average method. The proposed meta-learner CNNs achieved an accuracy of 86.32%.
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