Shumoos Al-Fahdawi

Corneal Confocal Microscopy detects a Reduction in Corneal Endothelial Cells and Nerve Fibres in Patients with Acute Ischemic Stroke

R. Malik, A. Shuaib, I. Petropoulos, 2018, Scientific Reports.

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Alaa S. Al-Waisy, M. Mohammed, Seifedine Kadry, 2022, Mathematics.

Al-Fahdawi, Shumoos and Qahwaji, Rami and Al-Waisy, Alaa S. and Ipson, Stanley and Malik, Rayaz A. and Brahma, Arun and Chen, Xin (2016) A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy

Alaa S. Al-Waisy, S. Ipson, Rami Qahwaji, 2018 .

A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology

Stanley S. Ipson, Rami Qahwaji, Shumoos Al-Fahdawi, 2018, Comput. Methods Programs Biomed..

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Stanley S. Ipson, Rami Qahwaji, Shumoos Al-Fahdawi, 2016, Comput. Methods Programs Biomed..

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Mazin Abed Mohammed, Dac-Nhuong Le, Begonya Garcia-Zapirain, 2021, Computers, Materials & Continua.

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, 2020, Soft Computing.

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Nureize Arbaiy, Mazin Abed Mohammed, Wajdi Alhakami, 2020, IEEE Access.

A multi-biometric iris recognition system based on a deep learning approach

Alaa S. Al-Waisy, Tarek A. M. Nagem, S. Ipson, 2017, Pattern Analysis and Applications.

A multimodal biometrie system for personal identification based on deep learning approaches

Stanley S. Ipson, Rami Qahwaji, Shumoos Al-Fahdawi, 2017, 2017 Seventh International Conference on Emerging Security Technologies (EST).

A multimodal deep learning framework using local feature representations for face recognition

Stanley S. Ipson, Rami Qahwaji, Shumoos Al-Fahdawi, 2017, Machine Vision and Applications.

Corneal Confocal Microscopy detects a Reduction in Corneal Endothelial Cells and Nerve Fibres in Patients with Acute Ischemic Stroke

R. Malik, A. Shuaib, I. Petropoulos, 2018, Scientific Reports.

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

Alaa S. Al-Waisy, Rami Qahwaji, M. Mohammed, 2022, Mathematics.

A modern deep learning framework in robot vision for automated bean leaves diseases detection

Alaa S. Al-Waisy, Sudad H. Abed, Shumoos Al-Fahdawi, 2021, International Journal of Intelligent Robotics and Applications.

A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms

Stanley S. Ipson, Rami Qahwaji, Shumoos Al-Fahdawi, 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

Fundus-DeepNet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images

Alaa S. Al-Waisy, R. Martínek, Rami Qahwaji, 2023, Inf. Fusion.

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Alaa S. Al-Waisy, S. Ipson, Rami Qahwaji, 2016, Comput. Methods Programs Biomed..

An Automatic Corneal Subbasal Nerve Registration System Using FFT and Phase Correlation Techniques for an Accurate DPN Diagnosis

Rami Qahwaji, Shumoos Al-Fahdawi, Alaa S. Al-Waisy, 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.