Using CNNs to classify an image into normal or glaucomatous, using retinal fundus images by transfer learning. The dataset used is called ACRIMA, containing 705 labelled images: 396 glaucomatous images and 309 normal images.
The CNN models were fit using 70% of the dataset for training, 10% for validation and 20% for testing.
The model architecture used is as follows:
- An input layer (256, 256, 3)
- A data augmentation layer involving random change in contrast, flip along horizontal or vertical, rotation, and translation of the images
- The base model with image-net weights
- A flatten layer
- A dense layer with ReLU activation
- A 0.5 dropout layer
- A dense output layer with softmax activation
In the case of ResNet-50 based model, a comparison was made between efficacy of different histogram equalization techniques.
- Histogram Equalization
- Contrast-Limited Adaptive Histogram Equalization