الفهرس 
Chapter (1) IntroductionOnly 14 pages are availabe for public view
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Abstract
Confocal microscopy imaging is modern technique that is used to image the human eye.
Both corneal and retinal images are obtained with this imaging technique. This thesis is
mainly concerned with the processing of corneal images. The images can be obtained as
separate images or video frames. These images are characterized with hexagonal shapes in
Endothelium layer. These images have low contrast. They may contain abnormalities or
anomalies. Hence, anomaly detection techniques can be implemented on these images for
the purpose of early diagnosis. The main objective of thesis is to perform the anomaly
detection task from corneal images, efficiently. Both Machine Learning (ML) and Deep
Learning (DL) approaches are introduced in the thesis for anomaly detection with high
accuracy. The first approach based on ML adopts Mel-Frequency Cepstral Coefficients
(MFCCs) as feature extraction with Support Vector Machine (SVM) classifier. The second
approach depends on Deep Learning (DL) concepts. Both Convolutional Neural Networks
(CNNs) and Long Short Term Memory (LSTM) are considered in the approach on the
frames of corneal videos. Simulation results proved that the utilization of a CNN with 5
layers gives the best classification results for anomaly detection. The results of the
proposed techniques will be compared with recent published results for the same data and
environments. For more investigation, the percentage of the training and testing will be
changed and test the performance. Moreover, a scenario for medical communication is
considered with a security framework based on watermarking.