Walid Al-Atabany received the B.Sc. and M.Sc. degrees from the Biomedical Engineering Department, Cairo University, in 1999 and 2004, respectively, and the Ph.D. degree in biomedical engineering from Imperial College London in 2010. In 2011, he was a Research Associate with Newcastle University for two years. He is currently a full professor at the Information Technology and Computer Science School at Nile University.
His research interests are highly interdisciplinary; however, the main interest focuses on the area of assistive techniques for visually impaired and prosthetic vision. Also, his research extended to include problems related to the advancement of statistical signal and image processing, as well as machine learning methods (including deep learning) and their application to large-scale pattern classification and signal interpretation problems (e.g. EEC and ECG signals). He received the 2nd Price Award from the 2nd Symposium of the Neuroscience Technology Network (NTN2009), the ARVO 2010 travel grant from the AFER/National Institute for Health Research Centre for Ophthalmology, and two Newton institutional link grants from the British Council, in 2015 and 2016, respectively. In 2021 he received the scientific excellence award offered by Helwan University. He has over 50 publications in highly impacted journals and conferences.
1) Received the Scientific Excellence Award for the Scientific award for the best researchers from Helwan University.
2) Received the Newton travel grant for Travel grant from British Embassy/STDF.
3) Received the Travel grant for Travel grant from AFER/National Institute for Health Research Biomedical Research Centre for Ophthalmology.
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems
Recently, the numerical optimization field has attracted the research community to propose and develop various metaheuristic optimization algorithms. This paper presents a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA). The proposed algorithm is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for
Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN
The objective of this research is to build a 'Whole Slide Images' classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four
Corneal Biomechanics Assessment Using High Frequency Ultrasound B-Mode Imaging
Assessment of corneal biomechanics for pre- and post-refractive surgery is of great clinical importance. Corneal biomechanics affect vision quality of human eye. Many factors affect corneal biomechanics such as, age, corneal diseases and corneal refractive surgery. There is a need for non-invasive in-vivo measurement of corneal biomechanics due to corneal shape preserving as opposed to ex-vivo
An E-health System for Encrypting Biosignals Using Triple-DES and Hash Function
This Electronic Health (E-Health) is a broad expression that enables the communication between healthcare professionals in handling patient information through the cloud. Exchanging medical data over the public cloud requires securing transferring for the data that's direct many researchers in proposing different secure schemes to enable users to handle data safely without hacking or alternating
Robust Background Template for Saliency Detection
In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super
Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning
The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network
Studying Genes Related to the Survival Rate of Pediatric Septic Shock
Pediatric septic shock is generally considered as a devastating clinical syndrome that can lead to tissue damage and organ failure due to the over exaggerated immune response to an infection. Therefore, in this paper, we attempted to early identify the clinical course of such disease with the aid of peripheral blood T-cells of 181 pediatric patients who admitted to Intensive Care Unit (ICU)
INVESTIGATION OF DIFFERENTIALLY EXPRESSED GENE RELATED TO HUNTINGTON'S DISEASE USING GENETIC ALGORITHM
neurodegenerative diseases have complex pathological mechanisms. Detecting disease-associated genes with typical differentially expressed gene selection approaches are ineffective. Recent studies have shown that wrappers Evolutionary optimization methods perform well in feature selection for high dimensional data, but they are computationally costly. This paper proposes a simple method based on a
- Retinal Prosthesis
- Image Processing
- Medical Imaging