الفهرس 
Chapter (1): IntroductionOnly 14 pages are availabe for public view
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Abstract
Automatic detection of maculpathy disease is a very important step to achieve
high-accuracy results for the early discovery of diseases and help ophthalmologists to
treat patients. Manual detection of diabetic maculpathy is time consuming and it needs
much effort from ophthalmologists Retinopathy is an eye disease for diabetics.
Retinopathy can occur with all types of diabetes and can lead to lack of vision if
leaved untreated. Diabetic maculopathy results from the retinopathy disease.
Maculopathy is damage to the macula. Macula is a sensitive part of the retina, which
is used for central vision and reading. It is near the retina center. Exudates are one of
the common visible signs of diabetic maculpathy. The major cause of exudates is
leaking of lipids and proteins from damaged retinal blood vessels. Detection of
exudates in eye images is used for diagnosis of the maculpathy disease. The proposed
framework begins with fuzzy image enhancement of eye images for contrast
enhancement in order to better represent objects of the images. Then, the segmentation
process is performed to determine the optic disc and blood vessels to remove them.
The next step is working on an image with exudates only if existing. A gradient
process is performed on the image. The histogram of gradients is evaluated. A
cumulative histogram is further generated for discrimination between image with and
without exudates. A threshold histogram curve is generated based on predefined
images with and without exudates for classification of images in the testing phase. A
Convolutional Neural Network (CNN) is used to classify the normal and abnormal
cases. The performance of the CNN is higher than traditional networks. The accuracy
of the CNN is higher than the accuracy of traditional networks. The main objective of
this thesis is to build up an efficient Computer Assisted Diagnosis (CAD) system for
the detection of anomalies from medical eye images to help ophthalmologists for
identifying diabetic maculpathy, easily.