Automatic identification of diabetic maculopathy stages using fundus images.
Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.
Nayak
J
J
Department of Electronics and Communication, Manipal Institute of Technology, Manipal, India.
Bhat
P S
PS
Acharya
U R
UR
D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000473:pathology; D003952:Diagnostic Imaging / Q000379:methods; D005122:Exudates and Transudates / Q000378:metabolism; D005260:Female; D005584:Fovea Centralis; D005654:Fundus Oculi; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008266:Macula Lutea / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity