Mr Dhafer Alhajim received his B.S. degree in Computer’s Technique Engineering from The Islamic University of najaf, Iraq in 2012, and the MSc degree as scholarship with the first rank in computer engineering in a subfield of computer Architecture from Tabriz university, Iran in 2018. He is currently a PhD candidate in Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Iran. His research interests are Image processing, Machine Learning, Deep Learning, IOT and Cloud Computing.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture
A Novel ConvLSTM-Based U-net for Improved Brain Tumor Segmentation Osama Majeed Hilal Almiahi, Alaa Taima Albu-Salih, Dhafer Alhajim IEEE Access, 2024 Using 2D scans or simple 3D convolutions are two limitations of previous works on segmentation of brain tumors by deep learning, which lead to ignoring the temporal distribution of the scans. This study proposes a novel extension to the well-known U-net model for brain tumor segmentation, utilizing 3D Magnetic Resonance Imaging (MRI) volumes as inputs. The method, called ConvLSTM-based U-net + up skip connections, incorporates the ConvLSTM blocks to capture spatio-temporal dependencies in the 3D MRI volumes, and up skip connections to capture low-level feature maps extracted from the encoding path, enhancing the information flow through the network to the standard U-net architecture. A novel intensity normalization technique is used to improve the comparability of scans. This technique normalizes image intensity by subtracting the grey-value of the most frequent bin from the image. The novel method is tested on the Multimodal Brain Tumor Segmentation (BRATS) 2015 dataset, showing that the use of ConvLSTM blocks improved segmentation quality by 1.6% on the test subset. The addition of skip connections further improved performance by 3.3% and 1.7% relative to the U-net and ConvLSTM-based U-net models, respectively. Moreover, the inclusion of up skip connections could enhance the performance by 5.7%, 3.99% and 2.2% relative to the simple U-net, ConvLSTM-based U-net, and ConvLSTM-based U-net with skip connections, respectively. Finally, the novel preprocessing technique had a positive effect on the proposed network, resulting in a 3.3% increase in the segmentation outcomes.
FFDR: Design and implementation framework for face detection based on raspberry pi Dhafer Alhajim, Gholamreza Akbarizadeh, Karim Ansari-Asl Iranian Conference on Machine Vision and Image Processing Mvip, 2022 In today’s world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. This paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language.
Industry, Institute, or Organisation Collaboration
