Semantic Segmentation of Retinal Blood Vessels from Fundus Images by using CNN and the Random Forest Algorithm

Skouta, Ayoub; Elmoufidi, Abdelali; Andaloussi, Said Jai; Ouchetto, Ouail

doi:10.5220/0010911800003118

Cited by 8 publications

(1 citation statement)

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“…A preprocessing step is necessary to obtain better data to facilitate subsequent operations. This step begins with the removal of the black border from the original data, followed by separation of the RGB color channels to extract the green channel, which has a strong contrast between hemorrhages and background in the fundus image compared to the red or blue channel [50,51] and ends with resizing of the image. The results of the preprocessing step are shown in Fig.…”

Section: Proposed Unet Architecturementioning

confidence: 99%

Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network

et al. 2022

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Because retinal hemorrhage is one of the earliest symptoms of diabetic retinopathy, its accurate identification is essential for early diagnosis. One of the major obstacles ophthalmologists face in making a quick and effective diagnosis is viewing too many images to manually identify lesions of different shapes and sizes. To this end, researchers are working to develop an automated method for screening for diabetic retinopathy. This paper presents a modified CNN UNet architecture for identifying retinal hemorrhages in fundus images. Using the graphics processing unit (GPU) and the IDRiD dataset, the proposed UNet was trained to segment and detect potential areas that may harbor retinal hemorrhages. The experiment was also tested using the IDRiD and DIARETDB1 datasets, both freely available on the Internet. We applied preprocessing to improve the image quality and increase the data, which play an important role in defining the complex features involved in the segmentation task. A significant improvement was then observed in the learning neural network that was able to effectively segment the bleeding and achieve sensitivity, specificity and accuracy of 80.49%, 99.68%, and 98.68%, respectively. The experimental results also yielded an IoU of 76.61% and a Dice value of 86.51%, showing that the predictions obtained by the network are effective and can significantly reduce the efforts of ophthalmologists. The results revealed a significant increase in the diagnostic performance of one of the most important retinal disorders caused by diabetes.

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Section: Proposed Unet Architecturementioning

confidence: 99%