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Electronic Theses and Dissertations

2022

Deep learning

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The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah Dec 2022

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …


Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche Aug 2022

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …


Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab Aug 2022

Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab

Electronic Theses and Dissertations

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than …


Developing A Robust Defensive System Against First Order Adversarial Attacks Using Siamese Neural Network Methods, Ahamd Khalil Jan 2022

Developing A Robust Defensive System Against First Order Adversarial Attacks Using Siamese Neural Network Methods, Ahamd Khalil

Electronic Theses and Dissertations

Both Convolutional neural networks (CNN) and Deep neural networks (DNN)have recently demonstrated state-of-the-art performance in various real-world ap- plications. However, in recent research the Deep neural networks are shown to be sensitive to adversarial attacks .[32]. Furthermore, it was evidenced that inputs that are almost invariant to the human eye from natural data can be classified incorrectly by deep neural networks. [32]. Although adversarial training improves the model’s robustness significantly, it eventually devolves into a whack-a-mole game in which defenders and attackers try to outdo each other. Because of recent advancements in computer applications, the security aspects of machine learning …