2023 - Research.com Computer Science in Saudi Arabia Leader Award
2022 - Research.com Computer Science in Saudi Arabia Leader Award
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Field. His study in Image segmentation, Support vector machine, Cursive, Preprocessor and Feature detection falls within the category of Artificial intelligence. Amjad Rehman interconnects Feature and Deep learning in the investigation of issues within Pattern recognition.
His Segmentation research incorporates elements of Silhouette, Pectoral muscle and Medical imaging. Much of his study explores Computer vision relationship to Robustness. The concepts of his Field study are interwoven with issues in Image and Data mining.
Amjad Rehman mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Feature extraction. Artificial intelligence is closely attributed to Machine learning in his study. His Pattern recognition research focuses on subjects like Feature, which are linked to Cluster analysis.
Amjad Rehman works mostly in the field of Segmentation, limiting it down to topics relating to Natural language processing and, in certain cases, Field, as a part of the same area of interest. His work carried out in the field of Computer vision brings together such families of science as Facial expression, Computer graphics and Emotional expression. His research integrates issues of Image processing and Histogram in his study of Support vector machine.
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Feature extraction. His study in Segmentation, Convolutional neural network, Feature selection, Support vector machine and Image retrieval are all subfields of Artificial intelligence. His biological study spans a wide range of topics, including Cursive, Natural language processing and White matter.
His studies deal with areas such as Region of interest, Image, Feature and Cluster analysis as well as Pattern recognition. He combines subjects such as Coherence and Multiclass classification with his study of Deep learning. His Feature extraction study integrates concerns from other disciplines, such as Confusion matrix, Image segmentation and Histogram equalization.
Amjad Rehman focuses on Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Machine learning. His Artificial intelligence study frequently draws connections between adjacent fields such as Fitness function. His Deep learning research includes themes of Contextual image classification, Image, Routing and X ray image.
His study looks at the relationship between Pattern recognition and fields such as Entropy, as well as how they intersect with chemical problems. The Convolutional neural network study combines topics in areas such as Classifier, Feature selection and Discrete cosine transform. His Segmentation research is multidisciplinary, relying on both White matter and Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Medical Image Segmentation Methods, Algorithms, and Applications
Alireza Norouzi;Mohd Shafry Mohd Rahim;Ayman Altameem;Tanzila Saba.
Iete Technical Review (2014)
Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN)
Sajid Iqbal;M. Usman Ghani;Tanzila Saba;Amjad Rehman.
Microscopy Research and Technique (2018)
Classification of acute lymphoblastic leukemia using deep learning
Amjad Rehman;Naveed Abbas;Tanzila Saba;Syed Ijaz ur Rahman.
Microscopy Research and Technique (2018)
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists.
Muhammad Attique Khan;Imran Ashraf;Majed Alhaisoni;Robertas Damaševičius;Robertas Damaševičius.
Diagnostics (Basel, Switzerland) (2020)
Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction
Tanzila Saba;Muhammad Attique Khan;Amjad Rehman;Souad Larabi Marie-Sainte.
Journal of Medical Systems (2019)
Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.
Muhammad A. Khan;Ikram U. Lali;Amjad Rehman;Mubashar Ishaq.
Microscopy Research and Technique (2019)
Neural networks for document image preprocessing: state of the art
Amjad Rehman;Tanzila Saba.
Artificial Intelligence Review (2014)
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks
Farheen Ramzan;Muhammad Usman Ghani Khan;Asim Rehmat;Sajid Iqbal;Sajid Iqbal.
Journal of Medical Systems (2020)
A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection
Muhammad Sharif;Muhammad Attique Khan;Tallha Akram;Muhammad Younus Javed.
Eurasip Journal on Image and Video Processing (2017)
Detection of copy-move image forgery based on discrete cosine transform
Mohammed Hazim Alkawaz;Ghazali Sulong;Tanzila Saba;Amjad Rehman.
Neural Computing and Applications (2018)
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