An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images

Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), myopia, hypertension, and cataracts. If these diseases are not diagnosed early, they may cause partial or complete loss of vision in patients. Fundus imaging is the primary method used to diagnose ophthalmologic diseases. In this study, a powerful R-CNN+LSTM-based approach is proposed that automatically detects eight different ophthalmologic diseases from fundus images. Deep features were extracted from fundus images with the proposed R-CNN+LSTM structure. Among the deep features extracted, those with high representative power were selected with an approach called NCAR, which is a multilevel feature selection algorithm. In the classification phase, the SVM algorithm, which is a powerful classifier, was used. The proposed approach is evaluated on the eight-class ODIR dataset. The accuracy (main metric), sensitivity, specificity, and precision metrics were used for the performance evaluation of the proposed approach. Besides, the performance of the proposed approach was compared with the existing approaches using the ODIR dataset.

Automated summary

The study proposed a new method for diagnosing ophthalmological diseases by analyzing deep features in fundus images and using the SVM algorithm for classification, accurately detecting eight different ophthalmological diseases. Additionally, a multilevel feature selection algorithm called NCAR was used to enhance the accuracy of detection.