Improved detection accuracy of red lesions in retinal fundus images with superlearning approach

Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions.

In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to base-learners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. In this work, we have handled the data imbalance problem and compared the final accuracy with different synthetic data creation ratios.

The suggested approach uses publicly available resources to perform quantitative assessments at lesions-level. The overall accuracy of red lesion segregation is 93.5%, which has increased to 97.88% when data imbalance problem is taken care-off.

The results of our system have achieved competitive performance compared with other modern approaches and handling of data imbalance further increases the performance of it.