Diabetic retinopathy (DR) is an eye complication associated with diabetes, resulting in blurred vision or blindness. The early diagnosis and treatment of DR can decrease the risk of vision loss dramatically.However, such diagnosis is a tedious and complicated task due to the variability of retinal changes across the stages of the diseases, and due to the high number of undiagnosed and untreated DR cases. In this paper, we develop a computationally efficient and scalable deep learning model using convolutional neural networks (CNN), for diagnosing DR automatically. Various preprocessing algorithms are utilized to improve accuracy, and a transfer learning strategy is adopted to speed up the process. Our experiment used the fundus image set available on online Kaggle datasets. As an ultimate conclusion of applicable performance metrics, our computational simulation achieved a relatively-high F1 score of 93.2% for stage-based DR classification. Povzetek: Opisana je metoda globokih nevronskih mrež za diagnozo težav vida zaradi sladkorne bolezni.

The recognition or classification of Arabic handwritten characters is extremely crucial in many applications and, at the same time, one of the biggest challenges that machine learning faces. The emergence of deep learning, particularly Convolutional Neural Networks (CNN), is considered a suitable technique to face these challenges. In this research paper, an investigation model is proposed to make recognition for Arabic handwriting utilizing one of CNN architectures: ResNet50 architecture, after replacing the last layer with one of two types of machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to reduce training time and increase overall accuracy. Our experimental work was performed on three data sets: Arabic Handwritten Character Dataset (AHCD), Alexa Isolated Alphabet Dataset (AIA9K), and Hijja Dataset. Experimental results show that combining ResNet50 with random forest produces more accurate and consistent results than the ResNet50 model produces by itself. Finally, the comparison with the other methods across all data sets demonstrates the robustness and effectiveness of the combination of random forest with the ResNet50 approach. Where the modified ResNet50 architecture has achieved a rate of 92.37%, 98.39%, and 91.64%, while the combination architecture has achieved 95%, 99%, and 92.4% for AIA9K, AHCD, and Hijja datasets, respectively. Povzetek: Avtorji so razvili novo metodo kot nadgradnjo Resnet50 z dodatkom zadnjega nivoja v obliki SVM in RF. Na več domenah je dosegla boljše rezultate kot osnovni Resnet50.

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Nowadays, optical character recognition is one of the most successful automatic pattern recognition applications. Many works have been done regarding the identification of Latin and Chinese characters. However, the reason for having few investigations for the recognition of Arabic characters is the complexity and difficulty of Arabic characters identification compared to the others. In the current work, we investigate combining multiple machine learning algorithms for multi-font Arabic isolated characters recognition, where imperfect and dimensionally variable input charactersare faced. To the best of our knowledge, there is no such work yet available in this regard. Experimental results show that combined multiple classifiers can outperform each individual classifier produces by itself. The current findings are encouraging and opens the door for further research tasks in this direction.

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