Article Per Year (5 Year)
p-Index From 2021 - 2026
0.553
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Bilal R. Altamer
University of Mosul
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A convolution neural network model for knee osteoporosis classification using X-ray images Omar Khalid M. Ali; Abeer K. Ibrahim; Bilal R. Altamer
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 4: August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i4.26778

Abstract

Bone structure deterioration along with low levels of bone density are the hallmarks of knee osteoporosis (KOP). The conventional approach for detecting osteoporosis is accomplished using a knee radiograph, but it requires specialized knowledge. Nevertheless, X-rays can be difficult to interpret due to their large volume and minor fluctuations. In the past few decades, deep learning algorithms have minimized misinterpretation and modified medical diagnosis. In particular, algorithms based on convolutional neural networks (CNNs) have been used to speed up the procedure of diagnosis because of their innate capacity to extract significant features that often are challenging to spot by hand. A robust CNN model was proposed in this paper for KOP classification which uses a train and test approach to recognize healthy, osteopenia-predicted, and osteoporosis knee cases using 1947 X-ray images. The proposed model was designed using Jupyter Notebook and is in Python. To verify the efficiency of the model, some factors were calculated such as accuracy, precision, recall, and f1-score. In comparison with other similar systems, the results obtained showed that the accuracy of the proposed system reached 90.25%.
Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis Bilal R. Altamer; Muhamad Azhar Abdilatef Alobaidy; Aws Hazim Saber Anaz; Zahraa Tarik AlAli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3342-3351

Abstract

Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.
Co-Authors Abeer K. Ibrahim Aws Hazim Saber Anaz Muhamad Azhar Abdilatef Alobaidy Omar Khalid M. Ali Zahraa Tarik AlAli