Women, trauma and alcohol dependency: Connections and disconnections in alcohol treatment for women

11^th Global Summit on Artificial Intelligence and Neural Networks

August 31, 2023 | Webinar

Hissa Mohammed

MSc in medical imaging, Qatar

Scientific Tracks Abstracts: Adv Robot Autom

Abstract :

The application of deep learning approaches, particularly convolutional neural networks (CNNs), has shown significant promise in automating the diagnosis process in radiography. This abstract explores the potential of deep learning techniques to enhance accuracy and efficiency in the field of radiographic diagnosis. By leveraging large datasets and complex neural network architectures, deep learning models can effectively detect abnormalities and diseases, thereby assisting radiologists in making more accurate diagnoses while reducing interpretation time. The abstract highlights several key aspects of deep learning in radiography. Firstly, it discusses the architectural advancements in CNNs, including deeper and more complex network designs, that have improved the performance of automated diagnosis systems. Secondly, it explores the benefits of transfer learning, where pretrained models trained on large datasets are finetuned for specific radiographic tasks, leading to improved performance with reduced data requirements. Moreover, the abstract emphasizes the importance of addressing challenges such as interpretability and explainability in deep learning models. By employing techniques such as attention mechanisms and explainable AI, the decision-making process of the neural network can be better understood and validated by radiologists.

Biography :

Hissa Mohammed is an experienced diagnostic radiographer with a strong academic background. They obtained their Bachelor's degree in Diagnostic Radiography from Queen Margaret University (2010-2014) and later pursued a Master's degree in Medical Imaging from the University of Aberdeen (2018-2019). Currently, Hissa serves as a Medical Radiography Instructor at the University of Doha for Sciences and Technology since December 2020. Their role involves educating and mentoring aspiring radiographers, contributing to the advancement of the field.