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| 1 | Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission显示文摘The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data.This article introduces a self-embedded image verification and integrity scheme.The images are firstly split into dedicated segments of the same block sizes.Then,different Analytic Beta-Wavelet(ABW)orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method.ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes.We conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes 64×64,128×128,and 256×256.We embed the watermark using the ABW-based image watermarking method in the 2-level middle frequency sub-bands of the ABW digital image coefficients.The 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 values.From the implementation results,we came to know that this ABW-based image watermarking method can withstand many image manipulations compared to other existing methods. | Hesham Alhumyani Ibrahim Alrube Sameer Alsharif Ashraf Afifi Chokri Ben Amar Hala S.El-Sayed Osama S.Faragallah | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 2 | Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning显示文摘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 classification.Many studies have shown promising results using Machine Learning and,recently,Deep Learning to detect malignancy in cancer cells.However,the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem.In literature,many attempts were made to classify up to four simple types of lymphoma.This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma.These Lymphoma types are Classical Hodgkin Lymphoma,Nodular Lymphoma Predominant,Burkitt Lymphoma,Follicular Lymphoma,Mantle Lymphoma,Large B-Cell Lymphoma,and T-Cell Lymphoma.Our proposed approach uses Residual Neural Networks,ResNet50,with a Transfer Learning for lymphoma’s detection and classification.The model used results are validated according to the performance evaluation metrics:Accuracy,precision,recall,F-score,and kappa score for the seven multi-classes.Our algorithms are tested,and the results are validated on 323 images of 224×224 pixels resolution.The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%. | Schahrazad Soltane Sameer Alsharif Salwa M.Serag Eldin | 2022 | Computer Systems Science & Engineering2022，40，2： | 0 |
| 3 | Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network显示文摘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 learning.It 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. | 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 | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 4 | Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions显示文摘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 descriptors.Usually,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 illumination.However,the performance and reliability of these descriptors decrease for some imaging variations such as point of view,zoom(scale),and rotation.The intro-duced description method improves image matching in the event of such distor-tions.It utilizes a contourlet-based detector to detect the strongest key points within a specified window size.The 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 transformation.The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation.Consequently,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 paper.The 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 dataset.The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature. | Ibrahim El rube Sameer Alsharif | 2022 | Computer Systems Science & Engineering2022，42，7： | 0 |
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