COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.

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Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal. The problem is that most of these experiments are done inEnglish, as it is the most spoken language in the world. But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them. In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers. The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers. A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset.

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