Segmentation-based Deep Learning Fundus Image Analysis

Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predictive power of the different compositions of retinal images using deep learning. This paper is to investigate whether there exists specific region that could assist in better prediction of the retinopathy disease, meaning to find the best region in fundus images that can boost the prediction power of models for retinopathy classification. To this end, with image segmentation techniques, the fundus image is divided into three different segments, namely, the optic disc, the blood vessels, and the other regions (regions other than blood vessels and optic disk). These regions are then contrasted against the performance of original fundus images. The convolutional neural network as well as transfer deep learning with the state-of-the-art pre-trained models (i.e., AlexNet, GoogleNet, Resnet50, VGG19) are deployed. We report the average of ten runs for each model. Different machine learning evaluation metrics are used. The other regions' segment reveals more predictive power than the original fundus image especially when using AlexNet/Resnet50.

Cheddad, AbbasBlekinge Tekniska Högskola,Institutionen för datavetenskap,BigData@BTH(Swepub:bth)abc (författare)
Blekinge Tekniska HögskolaInstitutionen för datavetenskap (creator_code:org_t)

Ingår i:2019 IEEE 27TH INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2019): IEEE, s. 44-539781728151656

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