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Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

Computer Systems Science & Engineering 2022年第7期42卷 407-421页

作者： Ibrahim El rube sameer alsharif Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.o.Box 11099Taif21944Saudi Arabia

This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing ***,the existent descriptors such as speeded up robust features(SURF),Kaze,binary robust invariant scalable keypoints(BRISK),features from accelerated segment test(FAST),and oriented FAST and rotated BRIEF(ORB)can competently detect,describe,and match images in the presence of some artifacts such as blur,compression,and ***,the performance and reliability of these descriptors decrease for some imaging variations such as point of view,zoom(scale),and *** intro-duced description method improves image matching in the event of such *** utilizes a contourlet-based detector to detect the strongest key points within a speciﬁed window *** selected key points and their neighbors con-trol the size and orientation of the surrounding regions,which are mapped on rec-tangular shapes using polar *** resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard ***,the descriptor obtained is invariant(translation,rotation,and scale)because of the two methods;the extraction of the region and the polar transformation techniques used in this *** descrip-tion method introduced in this article is tested against well-established and well-known descriptors,such as SURF,Kaze,BRISK,FAST,and ORB,techniques using the standard OXFORD *** presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.

关键词： Keypoint detection descriptors neighbor region similarity invariance

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Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

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Computer Systems Science & Engineering 2022年第2期40卷 629-644页

作者： Schahrazad Soltane sameer alsharif Salwa M.Serag Eldin College of Computers and Information Technology Computer Engineering DepartmentTaif UniversityTaifKingdom of Saudi Arabia

Current cancer diagnosis procedure requires expert knowledge and is time-consuming,which raises the need to build an accurate diagnosis support system for lymphoma identification and *** studies have shown promising results using Machine Learning and,recently,Deep Learning to detect malignancy in cancer ***,the diversity and complexity of the morphological structure of lymphoma make it a challenging classification *** literature,many attempts were made to classify up to four simple types of *** paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive *** Lymphoma types are Classical Hodgkin Lymphoma,Nodular Lymphoma Predominant,Burkitt Lymphoma,Follicular Lymphoma,Mantle Lymphoma,Large B-Cell Lymphoma,and T-Cell *** proposed approach uses Residual Neural Networks,ResNet50,with a Transfer Learning for lymphoma’s detection and *** model used results are validated according to the performance evaluation metrics:Accuracy,precision,recall,F-score,and kappa score for the seven *** algorithms are tested,and the results are validated on 323 images of 224×224 pixels *** results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.

关键词： Classification confusion matrices deep learning k-fold cross validation lymphoma diagnosis residual neural network transfer learning

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Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

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Computers, Materials & Continua 2022年第3期70卷 4657-4673页

作者： Hesham Alhumyani Ibrahim Alrube sameer alsharif Ashraf Afifi Chokri Ben Amar Hala S.El-Sayed Osama S.Faragallah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.11099Taif21944Saudi Arabia

The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of *** article introduces a self-embedded image verification and integrity *** images are firstly split into dedicated segments of the same block ***,different Analytic Beta-Wavelet(ABW)orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined *** orthogonal filter coefficients are estimated to improve image reconstruction under different block *** conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes 64×64,128×128,and 256×*** embed the watermark using the ABW-based image watermarking method in the 2-level middle frequency sub-bands of the ABW digital image *** imperceptibility and robustness of the ABW-based image watermarking method image is evaluated based on the Peak Signal to Noise Ratio(PSNR)and Correlation coefficient *** the implementation results,we came to know that this ABW-based image watermarking method can withstand many image manipulations compared to other existing methods.

关键词： Watermarking ABW median filtering JPEG compression PSNR

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda sameer alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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