M
Mohammed Elmogy
Researcher at Mansoura University
Publications - 195
Citations - 2431
Mohammed Elmogy is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Ontology (information science). The author has an hindex of 21, co-authored 168 publications receiving 1702 citations. Previous affiliations of Mohammed Elmogy include Kafrelsheikh University & University of Louisville.
Papers
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Brain tumor segmentation based on a hybrid clustering technique
TL;DR: The experimental results clarify the effectiveness of the proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.
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Current trends in medical image registration and fusion
TL;DR: The paper presents a description of the common diagnostic images along with the main characteristics of each of them and the current challenges associated with working with medical image registration and fusion through illustrating the recent diseases/disorders that were addressed through such an analyzing process.
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A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis
TL;DR: This paper proposes a fuzzy ontology-based CBR framework that combines a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types and achieves an accuracy of 97.67%.
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Image Stitching based on Feature Extraction Techniques: A Survey
TL;DR: A framework of a complete image stitching system based on feature based approaches will be introduced and the current challenges of image stitching will be discussed.
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Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study.
Harpal Sandhu,Nabila Eladawi,Mohammed Elmogy,Robert S. Keynton,omar helmy,Shlomit Schaal,Ayman El-Baz +6 more
TL;DR: A computer-aided diagnostic system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images is feasible and accurate and combining this system with OCT data is a plausible next step that would likely improve its robustness.