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  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for the automated segmentation of brain… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open Access

    ARTICLE

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507

    Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

  • Open Access

    ARTICLE

    Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

    Manas Ranjan Prusty1, Rishi Dinesh2, Hariket Sukesh Kumar Sheth2, Alapati Lakshmi Viswanath2, Sandeep Kumar Satapathy2,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718

    Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time augmentation, to improve segmentation accuracy.… More >

  • Open Access

    ARTICLE

    Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

    Dawei Liang1,2,4, Meng Wu1,*, Yan Hu3

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 279-293, 2023, DOI:10.32604/cmc.2023.035279

    Abstract As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and… More >

  • Open Access

    ARTICLE

    A Cross Language Code Security Audit Framework Based on Normalized Representation

    Yong Chen1,*, Chao Xu1, Jing Selena He2, Sheng Xiao3

    Journal of Quantum Computing, Vol.4, No.2, pp. 75-84, 2022, DOI:10.32604/jqc.2022.031312

    Abstract With the rapid development of information technology, audit objects and audit itself are more and more inseparable from software. As an important means of software security audit, code security audit will become an important aspect of future audit that cannot be ignored. However, the existing code security audit is mainly based on source code, which is difficult to meet the audit needs of more and more programming languages and binary commercial software. Based on the idea of normalized transformation, this paper constructs a cross language code security audit framework (CLCSA). CLCSA first uses compile/decompile technology to convert different high-level programming… More >

  • Open Access

    ARTICLE

    FSA-Net: A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

    Zhipeng Bin1, Huihuang Zhao1,2,*, Xiaoman Liang1,2, Wenli Chen1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 971-983, 2023, DOI:10.32604/iasc.2023.037270

    Abstract The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a novel attribute encoder is proposed… More >

  • Open Access

    ARTICLE

    Adaptive Noise Detector and Partition Filter for Image Restoration

    Cong Lin1, Chenghao Qiu1, Can Wu1, Siling Feng1,*, Mengxing Huang1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249

    Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >

  • Open Access

    ARTICLE

    Selection and Validation of Reference Genes for Normalization of RT-qPCR Analysis in Developing or Abiotic-Stressed Tissues of Loquat (Eriobotrya japonica)

    Shoukai Lin1,2,#, Shichang Xu1,#, Liyan Huang1, Fuxiang Qiu1, Yihong Zheng1, Qionghao Liu1, Shiwei Ma1,2, Bisha Wu1,2, Jincheng Wu1,2,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1185-1201, 2023, DOI:10.32604/phyton.2023.026752

    Abstract Loquat (Eriobotrya japonica Lindl.) is a subtropical evergreen fruit tree that produces fruits with abundant nutrients and medicinal components. Confirming suitable reference genes for a set of loquat samples before qRT-PCR experiments is essential for the accurate quantification of gene expression. In this study, eight candidate reference genes were selected from our previously published RNA-seq data, and primers for each candidate reference gene were designed and evaluated. The Cq values of the candidate reference genes were calculated by RT-qPCR in 31 different loquat samples, including 12 subgroups of developing or abiotic-stressed tissues. Different combinations of stable reference genes were screened… More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Wind Turbine Generator with Stacked Noise Reduction Autoencoder Based on Group Normalization

    Sihua Wang1,2, Wenhui Zhang1,2,*, Gaofei Zheng1,2, Xujie Li1,2, Yougeng Zhao1,2

    Energy Engineering, Vol.119, No.6, pp. 2431-2445, 2022, DOI:10.32604/ee.2022.020779

    Abstract In order to improve the condition monitoring and fault diagnosis of wind turbines, a stacked noise reduction autoencoding network based on group normalization is proposed in this paper. The network is based on SCADA data of wind turbine operation, firstly, the group normalization (GN) algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed, and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder, which further optimizes the problem that the loss function swings too much during the update process. Finally, in the last layer of… More >

  • Open Access

    ARTICLE

    Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

    P. Sharmila1,*, C. Deisy1, S. Parthasarathy2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 377-393, 2023, DOI:10.32604/csse.2023.027481

    Abstract With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty… More >

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