M
Muhammad Moazam Fraz
Researcher at University of the Sciences
Publications - 115
Citations - 3983
Muhammad Moazam Fraz is an academic researcher from University of the Sciences. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 21, co-authored 90 publications receiving 2804 citations. Previous affiliations of Muhammad Moazam Fraz include National University of Sciences and Technology & Kingston University.
Papers
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Journal ArticleDOI
Blood vessel segmentation methodologies in retinal images - A survey
Muhammad Moazam Fraz,Paolo Remagnino,Andreas Hoppe,Bunyarit Uyyanonvara,Alicja R. Rudnicka,Christopher G. Owen,Sarah Barman +6 more
TL;DR: The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures.
Journal ArticleDOI
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
Muhammad Moazam Fraz,Paolo Remagnino,Andreas Hoppe,Bunyarit Uyyanonvara,Alicja R. Rudnicka,Christopher G. Owen,Sarah Barman +6 more
TL;DR: This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses to segmentation of blood vessels in retinal photographs.
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An approach to localize the retinal blood vessels using bit planes and centerline detection
Muhammad Moazam Fraz,Sarah Barman,Paolo Remagnino,Andreas Hoppe,Abdul Basit,Bunyarit Uyyanonvara,Alicja R. Rudnicka,Christopher G. Owen +7 more
TL;DR: An automated method for segmentation of blood vessels in retinal images is reported and the results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.
Journal ArticleDOI
A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma.
Muhammad Shaban,Syed Ali Khurram,Muhammad Moazam Fraz,Muhammad Moazam Fraz,Muhammad Moazam Fraz,Najah Alsubaie,Iqra Masood,Sajid Mushtaq,Mariam Hassan,Asif Loya,Nasir M. Rajpoot,Nasir M. Rajpoot,Nasir M. Rajpoot +12 more
TL;DR: The proposed TILAb score is a digital biomarker which is based on more accurate classification of tumour and lymphocytic regions, is motivated by the biological definition of TILs as tumour infiltrating lymphocytes, with the added advantages of objective and reproducible quantification.
Journal ArticleDOI
Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy
R.A. Welikala,Muhammad Moazam Fraz,Jamshid Dehmeshki,Andreas Hoppe,Vikas Tah,Samantha Mann,Tom H. Williamson,Sarah Barman +7 more
TL;DR: An automated method for the detection of new vessels from retinal images is presented based on a dual classification approach that combines a support vector machine (SVM) classifier with a genetic algorithm based feature selection approach.