Retinopathy classification from fundus images put a lot of issues in front of ophthalmologists. Convolution and deep neural network models open the doors to handle such challenges and achieve great success in computer vision, but it is reaching its computational limits. This leads to the rethinking of less computationally intensive network architectures for computer vision problems. In this work we have used a RFMiD dataset, which is challenging for machine learning researchers due its multiclass, multi-labelled, and imbalanced nature. In the proposed work three models are developed to classify the retinopathy from fundus images. The first model inherits the properties of the VGG Net and Inception Net. This results in significant reduction in computational complexity compared with VGG Net and Inception net models. The second model is an improvised version of the previous one with an increase in depth that yields notable improvement in results, while maintaining the lower number of computations. The third model uses a bidirectional LSTM model as a classifier with 192 hand-crafted features. This model gives 0.985 AUC, with a precision of 0.98, and recall of 0.9 respectively.
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