Kamal Okba
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Kamal Okba has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Amal Hjouji
Laboratory of Information, Signals, Automation and Cognitivism, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah University (USMBA), Fez, Morocco
1 shared publication - Asmae Blilat
Laboratory of Science, Engineering and Management (LSIG), National School of Business and Management (ENCG), Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
1 shared publication - Jaouad El-Mekkaoui
Sidi Mohamed Ben Abdellah University
1 shared publication - Karim El Moutaouakil
Laboratory of Mathematics and Data Science, Polydisciplinary of Taza, Sidi Mohamed Ben Abdellah University, Taza 30000, Morocco
1 shared publication - Mohamed Benslimane
Sidi Mohamed Ben Abdellah University
1 shared publication - Omar El Ogri
Sidi Mohamed Ben Abdellah University
1 shared publication
Latest Publications
- Open Access
- 6 November 2025
- Published by MDPI AG in Mathematical and Computational Applications
- Vol. 30 (6) , 121
Efficient Biomedical Image Recognition Using Radial Basis Function Neural Networks and Quaternion Legendre Moments
Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases.
- 6 November 2025
- Published by MDPI AG in Mathematical and Computational Applications
- Vol. 30 (6) , 121
Open AccessEfficient Biomedical Image Recognition Using Radial Basis Function Neural Networks and Quaternion Legendre Moments
Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases.