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Hemorrhage Segmentation in Retinal Images Using Modified FCN-8

Chronic diabetes mellitus affecting the retina can lead to diabetic retinopathy. Advanced diabetic retinopathy can cause vision loss in patients. Therefore, automatic retinal diagnosis can detect diabetic retinopathy for further prevention. Several signs are effective in detecting diabetic retinopathy automatically, one of which is hemorrhage. Hemorrhages are significant lesions for the diagnosis of diabetic retinopathy. Hemorrhage detection is still a challenge because of the intensity of the color that resembles blood vessels. In this study, we propose a modification of the FCN-8 architecture by adding a modified identity mapping (convolution and batch normalization) to enrich the features of the hemorrhage segmentation process. In addition, contrast limited adaptive histogram equalization (CLAHE) and the extended version of CLAHE are applied to enhance the input images for our proposed method. We use the IDRiD dataset in the evaluation process. Using CLAHE enhancement, our proposed method achieved specificity, sensitivity, and accuracy with values of 98.81%,55.45%, and 98.25, respectively. The sensitivity value is higher than the sensitivity value of other methods.