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    A smart algorithm to predict the post-operative parameters of Intra-Corneal lens
    (Aksaray Üniversitesi Fen Bilimleri Enstitüsü, 2019) Abdulhameed, Hala Zaid; Akgün, Özlem
    Lens implantation has become easier than before due to the studies that conducted to avoid complications when and after the implantation operation,the most inevitable approach to avoid the risk of ICL implantation and overcome the complications is to accomplish the best possible separation between the back ICL surface and the anterior crystalline lens pole which it is called in the terminology of ophthalmology the vault. Recently, machine learning technique is broadly employed in research especially in medical images due to the ability to analyze the data and also using intelligent methods in order to make a suitable decision which assists physicians in the interpretation the images. in this thesis, the machine learning algorithms have been exploited in order to evaluate the postoperative patient eye status, in particular, through building a system with two split phases, feature extraction, and classification. Edge histogram descriptor (EHD) and color layout descriptor (CLD) used for extract the features from the images, whilst, Naïve Bayes and decision tree classifier used for classification, on the other hand, a Hidden Markov Model has employed for diagnoses the Post-Operative eye patient stats via using 3-States after obtaining and mining the characteristics of the input images using singular decomposition value, finally, the precision of the system has calculated for each model.