H
Haroon Ahmed Khan
Researcher at COMSATS Institute of Information Technology
Publications - 14
Citations - 94
Haroon Ahmed Khan is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Segmentation & Encoder. The author has an hindex of 3, co-authored 11 publications receiving 38 citations. Previous affiliations of Haroon Ahmed Khan include Lancaster University & Dongguk University.
Papers
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Journal ArticleDOI
A Review on Glaucoma Disease Detection Using Computerized Techniques
Faizan Abdullah,Rakhshanda Imtiaz,Hussain Ahmad Madni,Haroon Ahmed Khan,Tariq M. Khan,Mohammad A. U. Khan,Syed Saud Naqvi +6 more
TL;DR: A comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images is provided in this article, where readers can understand the challenges of image processing and machine learning stand-point and will be able to identify gaps in current research.
Journal ArticleDOI
Comparative abrasive wear resistance and surface analysis of dental resin-based materials.
Maleeha Nayyer,Shahreen Zahid,Syed Hammad Hassan,Salman Aziz Mian,Sana Mehmood,Haroon Ahmed Khan,Muhammad Kaleem,Muhammad Sohail Zafar,Muhammad Sohail Zafar,Abdul Samad Khan +9 more
TL;DR: The AFM presented higher precision compared to optical profilometers at a nanoscale level, but both methods can be used in tandem for a more detailed and precise roughness analysis.
Proceedings ArticleDOI
Exploiting Residual Edge Information in Deep Fully Convolutional Neural Networks For Retinal Vessel Segmentation
Tariq M. Khan,Syed S. Naqvi,Muhammad Arsalan,Muhamamd Aurangzeb Khan,Haroon Ahmed Khan,Adnan Haider +5 more
TL;DR: A new supervised method using a variant of the fully convolutional neural network is pro-posed with the advantages of reduced hyper-parameters, reduced computational/memory requirements, and robust performance in capturing tiny vessel information.
Journal ArticleDOI
A Housekeeping Prognostic Health Management Framework for Microfluidic Systems
TL;DR: A methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics is delivered, using an oscillation-based test methodology to extract diagnostic information that is processed using a linear discriminant analysis-based classifier.
Journal ArticleDOI
Use of Self-Calibration Data for Multifunctional MEMS Sensor Prognostics
TL;DR: In this paper, a solution to monitor the degradation of a multifunctional microelectromechanical systems (MEMS) sensor and recalibrate the sensor output accordingly is proposed.