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With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

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Diabetes is a chronic disease related to the rise of levels of blood glucose. The disease that leads to serious damage to the heart, blood vessels, eyes, kidneys, and nerves is one of the reasons of death among the people in the world. There are two main types of diabetes: Type 1 and Type 2. The former is a chronic condition in which the pancreas produces little or no insulin by itself. The latter usually in adults, occurs when insulin level is insufficient. Classification of diabetes mellitus data which is one of the reasons of death among the people in the world is important. This study which successfully distinguishes diabetes or normal persons contains two major steps. In the first step, the feature selection or weighting methods are analyzed to find the most effective attributes for this disease. In the further step, the performances of AdaBoost, Gradient Boosted Trees and Random Forest ensemble learning algorithms are evaluated. According to experimental results, the prediction accuracy of the combination of Stability Selection method and AdaBoost learning algorithm is a little better than other algorithms with the classification accuracy by 73.88%.

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