Guest Editors
Dr. Shuihua Wang, University of Leicester, United Kingdom
Dr. Zheng Zhang, Harbin Institute of Technology, China
Dr. Yuankai Huo, University of Vanderbilt, United States
Summary
The chest radiograph has been one of the most frequently performed radiological investigation tools. In clinical medicine, the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases (such as Atelectasis, Nodule, and Pneumonia, etc.). Importantly, it is of paramount importance for clinical screening, diagnosis, treatment planning, and efficacy evaluation. However, it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologists. In recent years, many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms. How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.
To address the unique problems in diagnosis of chest radiological images, various techniques need to be developed to advance such research fields. This special issue intends to demonstrate the new development and application of automatic chest radiograph processing, and thereby promote the research and development of chest radiology imaging in biomedical intelligence. The goal is to provide a platform for researchers to disseminate their recent advances and views of computer-assisted imaging processing and machine learning applications on diagnosis of chest Radiograph by publishing high-quality research papers in this interdisciplinary field.
Potential topics include, but are not limited to:
• Diagnosis of chest radiological image based on machine learning;
• Supervised or semi-supervised learning for lung, nodule, and lesion segmentation;
• Transfer learning for chest radiological image analysis;
• Multi-task learning, i.e., joint image segmentation and image-based diagnosis for chest radiological image;
• Early prediction of Tuberculosis or Nodule progress, i.e., from mild to severe or critical based on machine learning;
• Noisy label processing, i.e., diagnosis of chest radiological image with noisy (or incorrect) labels in some training samples;
• The publication of large-scale chest radiological image databases;
• Automated chest radiological image annotation and reporting based on machine learning;
• Automated chest radiological image enhancement and reconstruction based on machine learning;
• Automated chest radiological image annotation and reporting based on Artificial intelligence;
• Automated chest radiological image enhancement and reconstruction based on Artificial intelligence.
Published Papers
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Open Access
EDITORIAL
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Open Access
ARTICLE
COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things
Xiaowei Gu, Shuwen Chen, Huisheng Zhu, Mackenzie Brown
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 507-530, 2022, DOI:10.32604/cmes.2022.018948
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Coronavirus disease 2019 brings a huge burden on the medical industry all over the world. In the background
of artificial intelligence (AI) and Internet of Things (IoT) technologies, chest computed tomography (CT) and
chest X-ray (CXR) scans are becoming more intelligent, and playing an increasingly vital role in the diagnosis
and treatment of diseases. This paper will introduce the segmentation of methods and applications. CXR and CT
diagnosis of COVID-19 based on deep learning, which can be widely used to fight against COVID-19.
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Open Access
ARTICLE
An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification
Yalan Gao, Yanqiong Zhang, Xianwei Jiang
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 95-117, 2022, DOI:10.32604/cmes.2022.019970
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract (Aim) Chinese sign language is an essential tool for hearing-impaired to live, learn and communicate in deaf
communities. Moreover, Chinese sign language plays a significant role in speech therapy and rehabilitation. Chinese
sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society. Similar to the research of many biomedical
image processing (such as automatic chest radiograph processing, diagnosis of chest radiological images, etc.),
with the rapid development of artificial intelligence, especially deep learning technologies and algorithms, sign
language image recognition ushered in the spring. This…
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Open Access
ARTICLE
Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold
Shouming Hou, Chaolan Jia, Kai Li, Liya Fan, Jincheng Guo, Mackenzie Brown
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 81-94, 2022, DOI:10.32604/cmes.2022.019006
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak
edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important.
Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary
detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB).
As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is…
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Open Access
REVIEW
Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey
Darshan Babu L. Arasu, Ahmad Sufril Azlan Mohamed, Nur Intan Raihana Ruhaiyem, Nagaletchimee Annamalai, Syaheerah Lebai Lutfi, Mustafa M. Al Qudah
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 633-652, 2022, DOI:10.32604/cmes.2021.016985
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Stress is a normal reaction of the human organism which triggered in situations that require a certain level of
activation. This reaction has both positive and negative effects on everyone’s life. Therefore, stress management
is of vital importance in maintaining the psychological balance of a person. Thermal-based imaging technique is
becoming popular among researchers due to its non-contact conductive nature. Moreover, thermal-based imaging
has shown promising results in detecting stress in a non-contact and non-invasive manner. Compared to other
non-contact stress detection methods such as pupil dilation, keystroke behavior, social media interaction and
voice modulation, thermal-based imaging provides better features…
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Open Access
ARTICLE
COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network
Shouming Hou, Ji Han
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 855-869, 2022, DOI:10.32604/cmes.2022.016621
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Many people around the world have lost their lives due to COVID-19. The symptoms of most COVID-19 patients are fever, tiredness and dry cough, and the disease can easily spread to those around them. If the infected people can be detected early, this will help local authorities control the speed of the virus, and the infected can also be treated in time. We proposed a six-layer convolutional neural network combined with max pooling, batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients. In the 10-fold cross-validation methods, our method is superior to several state-of-the-art methods. In…
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Open Access
ARTICLE
BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images
Junding Sun, Xiang Li, Chaosheng Tang, Shixin Chen
CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 729-753, 2021, DOI:10.32604/cmes.2021.016416
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Purpose: As to January 11, 2021, coronavirus disease (COVID-19) has caused more than 2 million deaths worldwide. Mainly diagnostic methods of COVID-19 are: (i) nucleic acid testing. This method requires high requirements on the sample testing environment. When collecting samples, staff are in a susceptible environment, which increases the risk of infection. (ii) chest computed tomography. The cost of it is high and some radiation in the scan process. (iii) chest X-ray images. It has the advantages of fast imaging, higher spatial recognition than chest computed tomography. Therefore, our team chose the chest X-ray images as the experimental dataset in…
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Open Access
ARTICLE
The Research of Automatic Classification of Ultrasound Thyroid Nodules
Yanling An, Shaohai Hu, Shuaiqi Liu, Jie Zhao, Yu-Dong Zhang
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 203-222, 2021, DOI:10.32604/cmes.2021.015159
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)
and discriminate them as benign or malignant. The system firstly uses variational level set active contour with
gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.
Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method
based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this
paper introduces a weight factor to improve support vector machine, offering different classes of samples with
different weights. Finally,…
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Open Access
ARTICLE
ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module
Yudong Zhang, Xin Zhang, Weiguo Zhu
CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807
（This article belongs to this Special Issue:
Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for
COVID-19 (ANC).
Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to
avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure
of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis.
Results: The accuracy
of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively.
Conclusions: This
proposed ANC method is superior to 9 state-of-the-art approaches.
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