In this paper, we propose a novel classification algorithm based on convolutional neural networks (CNNs) to diagnose the severity of diabetic retinopathy (DR). We adopt a series of preprocessing operations to improve the quality of dataset. In addition, data augmentation is implemented on the training data to tackle the problem of imbalanced dataset. We design a CNNs model named DR-Net with a new Adaptive Cross-Entropy Loss, which emphasizes the difference of the penalty when training data are misclassified into different intervals. We train DR-Net on the publicly available Kaggle dataset. Experimental results show that our DR-Net achieves an accuracy of 0.821 and a kappa score of 0.663 on 3338 testing images.
In order to extract the effective information in different modalities of the tumor region in brain Magnetic resonance imaging (MRI) images, we propose a brain MRI tumor image fusion method combined with Shearlet and wavelet transform. First, the source images are transformed into Shearlet domain and wavelet domain. Second, the low frequency component of Shearlet domain is fused by Laplace pyramid decomposition. Then the low-frequency fusion image is obtained through inverse Shearlet transform. Third, the high frequency subimages in wavelet domain are fused. Then the high-frequency fusion image is obtained through inverse wavelet transform. Finally, the low-frequency fusion image and high-frequency fusion image are summated to get the final fusion image. Through experiments conducted on 10 brain MRI tumor images, the result shown that the proposed fusion algorithm has the best fusion effect in the evaluation indexes of spatial frequency, edge strength and average gradient. The main spatial frequency of 10 images is 29.22, and the mean edge strength and average gradient is 103.77 and 10.42. Compared with different fusion methods, we find that the proposed method effectively fuses the information of multimodal brain MRI tumor images and improves the clarity of the tumor area well.
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