M
Mohammed M. Abdelsamea
Researcher at Birmingham City University
Publications - 41
Citations - 1617
Mohammed M. Abdelsamea is an academic researcher from Birmingham City University. The author has contributed to research in topics: Image segmentation & Convolutional neural network. The author has an hindex of 13, co-authored 36 publications receiving 909 citations. Previous affiliations of Mohammed M. Abdelsamea include IMT Institute for Advanced Studies Lucca & Assiut University.
Papers
More filters
Journal ArticleDOI
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.
TL;DR: In this article, a deep CNN, called Decompose, Transfer, and Compose (DeTraC), was used for the classification of COVID-19 chest X-ray images.
Posted ContentDOI
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
TL;DR: This paper validate and adapt the previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images, and shows the capability of DeTraC in the detection of CO VID-19 cases from a comprehensive image dataset collected from several hospitals around the world.
Journal ArticleDOI
Image-based plant phenotyping with incremental learning and active contours
TL;DR: A method for the segmentation and the automated analysis of time-lapse plant images from phenotyping experiments in a general laboratory setting that can adapt to scene variability and is able to handle images with complicated and changing background in an automated fashion is proposed.
Journal ArticleDOI
Artificial intelligence in digital breast pathology: Techniques and applications.
Asmaa Ibrahim,Paul Gamble,Ronnachai Jaroensri,Mohammed M. Abdelsamea,Craig H. Mermel,Po-Hsuan Cameron Chen,Emad A. Rakha +6 more
TL;DR: The current and prospective uses of AI in digital pathology for breast cancer, the basics of digital pathology and AI, and outstanding challenges in the field are reviewed.
Posted Content
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
TL;DR: In this article, DeTraC was used for the classification of COVID-19 chest X-ray images from normal, and severe acute respiratory syndrome cases, achieving a high accuracy of 95.12% with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%.