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عضویت
فهرست مطالب نویسنده:

mohammed ahmed

  • Mohammed Ahmed *, Ahmed Fakhrudeen
    In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. Imaging techniques (like Chest X-rays) play an essential role in diagnosing many diseases (such as COVID-19). There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. In this paper, we start by overviewing the status of COVID-19 from a historical standpoint and diagnosis updates. Moving on, provide an overview of the convolutional neural networks. Then, we elaborate Transfer learning method and its main approaches. Next, we provide a critical literature review on implementing Deep learning techniques: 1) Novel deep learning architecture; 2) Direct use of deep learning; 3) Transfer learning fine-tuning technique, and 4) Transfer learning feature extraction technique. For each of these, we evaluate and compare very recent studies published in highly ranked journals. The experiments show that all techniques achieve closer accuracy, ranging from (98-100 \%). Along with all, the direct use of the deep learning technique records the highest accuracy and is less time-consuming and resource spending. Therefore, establishing such a technique is useful to predict the outbreak early, which in turn can aid in controlling the pandemic effectively.
    Keywords: COVID-19, Deep learning, machine learning, X-rays
  • Mohammed Ahmed *, Ahmed Fakhrudeen
    In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. The algorithms use Imaging techniques (like Chest X-Rays) in classifying COVID-19. Therefore, many global COVID-19 datasets have been released. However, so far, no public local Iraqi dataset has been developed. Therefore, our contribution is two folds. First, we investigate the techniques of deep learning techniques in COVID-19 classification. Second, we develop a new COVID-19 dataset, namely, “Covid-19IraqKirkukDataset” collected from hospitals in Kirkuk, Iraq. To the best of our knowledge, our dataset is the first COVID-19 dataset. Then, the evaluation of Covid19IraqKirkukDataset using Convolutional Neural Networks (CNNs) demonstrates promising classification outcomes.
    Keywords: COVID-19, Deep learning, Convolutional Neural Networks, Dataset, X-rays
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