الفهرس 
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Abstract
Deep learning techniques require a huge volume of data and powerful devices to work efficiently. These requirements are difficult to realize in medical research. For that, a transfer learning technique is utilized to takes advantage of a formerly learned model’s knowledge to solve another probably related job by demand minimal re-training or fine-tuning. Deep learning achieves significant results in diagnosis of different diseases. Various types of respiratory diseases, such as COVID-19, are among the diseases for which intelligent decision support systems are widely used in the detection and treatment. Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients. Machine learning (ML) has been used to examine chest X-ray frames. In this thesis, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest x-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal person. First, three different pre-trained Convolutional Neural Network (CNN) models (resnet18, resnet25, densenet201) are employed for deep feature extraction. Second, each feature vector is passed through the binary Butterfly optimization algorithm (bBOA) to reduce the redundant features and extract the most representative ones, and enhance the performance of the CNN models. Third, These selective features are then passed to an improved Extreme learning machine (ELM) using a BOA to classify the chest X-ray images. The proposed paradigm achieves 99.58% accuracy in detecting covid-19 cases.