Professional Overview
I am Assia YAKOUB, a software and data processing engineer with four years of hands-on experience in computer vision, deep learning, machine learning, and web application development. Through my academic and professional journey, I have been able to develop and implement cutting-edge solutions using deep learning techniques, particularly in the field of computer vision. My expertise includes designing and deploying convolutional neural networks (CNNs) for medical images to extract meaningful features and utilize them to address complex problems. Additionally, I have utilized advanced data preprocessing techniques to enhance model performance. Notably, I've adeptly navigated the challenges associated with limited datasets by leveraging various strategies. I take pride in my ability to fine-tune pre-trained models, optimize hyperparameters, and integrate the latest advancements in deep learning to deliver innovative and precise solutions. A significant highlight of my career is the application of deep learning to medical image diagnosis, focusing on COVID-19 cases. In this project, I employed a CNN model to differentiate chest X-ray images as normal or indicative of COVID-19. Furthermore, I developed an advanced AI method for the automated detection and classification of glaucoma in color fundus photographs. This achievement was made possible through a blend of progressive and deep learning techniques, as outlined in my research paper, "Progressive Deep Transfer Learning for Accurate Glaucoma Detection in Medical Imaging." I also played a pivotal role in "Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance." These papers, which have been accepted by ISPA 2024 and are currently pending publication, demonstrate my commitment to advancing medical imaging technology.