Ali Al-Kubaisi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ali Al-Kubaisi has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ahmeed Suliman Farhan
University of Hull Faculty of Science and Engineering
1 shared publication - Muhammad Khalid
University of Hull
1 shared publication - Muhammad Khan
University of the West of England
1 shared publication - Nassr Nafeaa Khamis
College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
1 shared publication - Umar Farooq Khattak
Unitar International University
1 shared publication - Umar Manzoor
University of Wolverhampton
1 shared publication
Latest Publications
- Open Access
- 28 December 2021
- Published by MDPI AG in Electronics
- Vol. 11 (1) , 85
A Transfer Learning Approach for Lumbar Spine Disc State Classification
Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.
- 28 December 2021
- Published by MDPI AG in Electronics
- Vol. 11 (1) , 85
Open AccessA Transfer Learning Approach for Lumbar Spine Disc State Classification
Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.