Ahmed Taha Elthakeb Naguib Youssef

Bio: Ahmed Taha Elthakeb Naguib Youssef is an academic researcher. The author has contributed to research in topics: Artificial intelligence & Workload. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Nano-scale TG-FinFET: Simulation and Analysis

Abstract: Center of Nano-electronics and Devices (CND) The American University in Cairo Zewail City of Science and Technology

Deep learning techniques for the fully automated detection and segmentation of brain MRI

Abstract: Over the past decade, auto-segmentation for tumors has drawn a lot of attention due to its significant impact on cancer treatment. Auto-segmentation architectures have a significant role in alleviating the enormous workload on the medical staff. This has motivated us to explore the latest solutions in auto-segmentation to use it in auto-segmentation. It works on automatically contouring tumors to make radiology treatment more attainable since manual contouring is repetitive and subjective to human error. Auto-segmentation usually strives to achieve high accuracy to reduce the time the radiologists take to contour the tumor. Saving time is critical as instead of contouring all the tumors, the radiologist can spend the time editing on the segmented tumor thus more patients can be diagnosed in less amount of time. There have been a lot of auto-segmentation architectures created for general purposes like the Segnet which is sometimes used in medical segmentation, but such architectures fail to achieve high accuracy especially in the details of the tumor. The U-Net is an auto-segmentation architecture specifically created for auto-segmentation on medical images like MRI and CT. The U-Net architecture can achieve high accuracy of segmentation with fewer amounts of data. We improved U-Net performance by using residual blocks on each layer of the architecture itself usually referred to as Res-U-Net. Our final proposed fine-tuned Res-U-Net model has achieved 97.10% on the used data which was the best of our 3 proposed fine-tuned models. The used data was Low-grade gliomas (LGGS) brain tumor dataset.

Erratum

Abstract: Gefäßmedizin in der Zukunft – Möglichkeiten mit 3D-Druckverfahren Bernhard Dorweiler, Hazem El Beyrouti, Christian Friedrich Vahl, Pia-Elisabeth Baqué, Ahmed Ghazy Zentralbl Chir; Epub ahead of print 09.12.2019 doi:10.1055/a-1025-2066 Im obigen Artikel war die Institutsbezeichnung und die Schreibweise des Autorennamens Pia-Elisabeth Baqué nicht korrekt. Richtig lautet sie wie folgt: Pia-Elisabeth Baqué, Klinik und Poliklinik für Nuklearmedizin, Universitätsmedizin Mainz, Deutschland.

The Monte Carlo method for semiconductor device simulation

Abstract: There can be little doubt that the Monte Carlo method for semiconductor device simulation has enormous power as a research tool. It represents a detailed physical model of the semiconductor material(s), and provides a high degree of insight into the microscopic transport processes. However, if the authority ascribed to Monte Carlo models of devices at 1/spl mu/m feature size is to be maintained for devices below O.1/spl mu/m, modelling of the fundamental physics must be further improved. And if the Monte Carlo method is to be successful as a semiconductor device design tool, the device model must be made more realistic. Success in the industrial sector depends on this, but also on achieving fast run-times optimisation - where the scope and need for ingenuity is now greatest.