Special Issues
Table of Content

Computer Modeling of Artificial Intelligence and Medical Imaging

Submission Deadline: 15 March 2023 (closed) View: 69

Guest Editors

Prof. Yu-Dong Zhang, University of Leicester, UK
Prof. Juan Manuel Gorriz, University of Granada, Spain
Prof. Zhengchao Dong, Columbia University and New York State Psychiatric Institute, USA
Prof. Qilong Wang, Nanjing Medical University, China
Prof. Shu-Wen Chen, Jiangsu Second Normal University, China

Summary

Over the recent years, we have saw the artificial intelligence (AI) methods reforming the zone of medical imaging. Many AI-based models have been created and improved to related medical image analysis and interpretation. Particularly, deep learning (DL) methods have exhibited brilliant performances in the screening and diagnosing numerous disorders and diseases. A challenge of AI-driven products is to develop more accurate diagnosis systems through DL models by taking benefits of learning patterns and relationships directly from medical imaging data,.


This Special Section aims to invite original research papers that report the latest advances of medical imaging-oriented AI models. Submissions should clarify the substantive improvements on work that has already been published, accepted for publication, or submitted in parallel to other conferences or journals.

 

The topics of interest include, but are not limited to following

Ø Advanced AI and DL models

Ø Supervised or semi-supervised learning

Ø Diagnosis using biomarkers and imaging-based methods

Ø Transfer learning methods for diagnosis and segmentation

Ø Genotype, phenotype, and pathogenesis

Ø Explainable/Trustworthy AI-based prediction, segmentation, and diagnosis

Ø Medical and healthcare equipment/resources supply chain management

Ø Wearable sensors or IoT based public health support, patient behavior and emotion monitoring

Ø VR/AR computer-aided diagnosis system

Ø 2D and 3D visualization

Ø Design and development of vaccine & targeted drug

Ø Epidemic dynamics prediction and forecast

Ø Graph neural network

Ø Computational prediction of protein structure associated with virus

Ø Socio-economic impacts of infectious disease interventions

Ø Survival and risk of recurrence estimation

Ø Recovery prediction in rehabilitation

Ø Potential therapeutics

Ø Public health system or strategies

Ø Medical image registration

Ø Radiomics



Published Papers


  • Open Access

    ARTICLE

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

    Nilkanth Mukund Deshpande, Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, Chang-Wook Lee
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood… More >

    Graphic Abstract

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

  • Open Access

    ARTICLE

    Attention-Based Residual Dense Shrinkage Network for ECG Denoising

    Dengyong Zhang, Minzhi Yuan, Feng Li, Lebing Zhang, Yanqiang Sun, Yiming Ling
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2809-2824, 2024, DOI:10.32604/cmes.2023.029181
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by More >

  • Open Access

    ARTICLE

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

    Sixian Chan, Binghui Wu, Guodao Zhang, Yuan Yao, Hongqiang Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection… More >

    Graphic Abstract

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

  • Open Access

    ARTICLE

    An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

    Lei Ling, Lijun Huang, Jie Wang, Li Zhang, Yue Wu, Yizhang Jiang, Kaijian Xia
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a… More >

  • Open Access

    ARTICLE

    TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation

    Peng Geng, Ji Lu, Ying Zhang, Simin Ma, Zhanzhong Tang, Jianhua Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions… More >

  • Open Access

    ARTICLE

    COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network

    Jiaji Wang, Shuwen Chen, Yu Cao, Huisheng Zhu, Dimas Lima
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2595-2616, 2023, DOI:10.32604/cmes.2023.025804
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other More >

  • Open Access

    REVIEW

    Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

    Jiaqi Shao, Shuwen Chen, Jin Zhou, Huisheng Zhu, Ziyi Wang, Mackenzie Brown
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2173-2219, 2023, DOI:10.32604/cmes.2023.025499
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract As a mainstream research direction in the field of image segmentation, medical image segmentation plays a key role in the quantification of lesions, three-dimensional reconstruction, region of interest extraction and so on. Compared with natural images, medical images have a variety of modes. Besides, the emphasis of information which is conveyed by images of different modes is quite different. Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors. Therefore, large quantities of automated medical image segmentation methods have been developed. However, until now, researchers have not developed… More >

  • Open Access

    REVIEW

    A Survey of Convolutional Neural Network in Breast Cancer

    Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2127-2172, 2023, DOI:10.32604/cmes.2023.025484
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Problems: For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims: A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it… More >

    Graphic Abstract

    A Survey of Convolutional Neural Network in Breast Cancer

  • Open Access

    ARTICLE

    Soft Tissue Feature Tracking Based on Deep Matching Network

    Siyu Lu, Shan Liu, Pengfei Hou, Bo Yang, Mingzhe Liu, Lirong Yin, Wenfeng Zheng
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 363-379, 2023, DOI:10.32604/cmes.2023.025217
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by… More >

    Graphic Abstract

    Soft Tissue Feature Tracking Based on Deep Matching Network

  • Open Access

    ARTICLE

    Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

    Yanwen Lu, Wenliang Ma, Xiang Dong, Mackenzie Brown, Tong Lu, Weidong Gan
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 347-362, 2023, DOI:10.32604/cmes.2023.024909
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework More >

  • Open Access

    REVIEW

    A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged: Techniques and Technologies

    Geng Chen, Lili Cheng, Rui Shao, Qingbin Wang, Shuihua Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1901-1940, 2023, DOI:10.32604/cmes.2023.024901
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract With the development of urbanization, the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern. Aging leads to gradual degeneration of the central nervous system, shrinkage of brain tissue, and decline in physical function in many elderlies, making them susceptible to neurological diseases such as Alzheimer’s disease (AD), stroke, Parkinson’s and major depressive disorder (MDD). Due to the influence of these neurological diseases, the elderly have troubles such as memory loss, inability to move, falling, and getting lost, which seriously affect their quality of life. Tracking… More >

    Graphic Abstract

    A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged: Techniques and Technologies

  • Open Access

    ARTICLE

    Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

    Zhengtao Xi, Chaofan Song, Jiahui Zheng, Haifeng Shi, Zhuqing Jiao
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2243-2266, 2023, DOI:10.32604/cmes.2023.023544
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution… More >

    Graphic Abstract

    Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

  • Open Access

    ARTICLE

    A Study of BERT-Based Classification Performance of Text-Based Health Counseling Data

    Yeol Woo Sung, Dae Seung Park, Cheong Ghil Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 795-808, 2023, DOI:10.32604/cmes.2022.022465
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract The entry into a hyper-connected society increases the generalization of communication using SNS. Therefore, research to analyze big data accumulated in SNS and extract meaningful information is being conducted in various fields. In particular, with the recent development of Deep Learning, the performance is rapidly improving by applying it to the field of Natural Language Processing, which is a language understanding technology to obtain accurate contextual information. In this paper, when a chatbot system is applied to the healthcare domain for counseling about diseases, the performance of NLP integrated with machine learning for the accurate More >

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