Deep learning to diagnose Peripapillary Atrophy in retinal images along with statistical features

Peripapillary Atrophy (PPA, hereafter) is one of the major indicators of an irreversible eye disease named Glaucoma. An early detection of PPA is vital to avoid vision reduction caused by pathological myopia, or a permanent loss caused by Glaucoma. PPA is a pigmented crescent-shaped abnormality around the optic disc region. In this paper, we propose a fusion method to detect the atrophy by combining ResNet50-based deep features along with clinically significant statistical features of the region of interest (containing PPA). We show results of extensive experimentation with six publicly available databases, on which the system is also trained. The testing is on a rather difficult dataset of community camp-based images captured under poor lighting conditions with hand-held low-resolution ophthalmoscopes. We show encouraging experimental results of the combination of the generalization power of deep features and the medical science behind clinical hand-crafted features. Such a feature combination out-performs any one of the modalities in the difficult experimental set. We compare our results with the state-of-the-art in the area. The proposed method outperforms existing methods with average sensitivity, specificity and accuracy values of 95.83% each. To the best of our knowledge, this is the best accuracy reported in the literature, on large and varied datasets.

Automated summary

This study proposes a fusion method combining deep features and clinically significant statistical features to detect Peripapillary Atrophy, showing promising results with average sensitivity, specificity, and accuracy values of 95.83%. The method outperforms existing techniques and achieves the best accuracy reported in the literature on large and varied datasets.