Guest Editors
Prof. Yu-Dong Zhang (Eugene), University of Leicester, UK
Prof. Zhengchao Dong, Columbia University, USA
Prof. Juan Manuel Gorriz, Cambridge University, UK/ University of Granada, Spain
Prof. Carlo Cattani, Tuscia University (VT), Italy
Prof. Ming Yang, Children’s Hospital of Nanjing Medical University, China
Summary
Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains, e.g., prediction, classification, detection, segmentation, diagnosis, interpreation, reconstruction, etc. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications.
The accelerating power of deep learning in diagnosing disease and analyzing medical data will empower physicians and speed-up decision making in clinical environments. Application of modern medical instruments and digitalization of medical care generated large amounts of biomedical information in recent years. However, new deep learning methods and computational models for efficient data processing, analysis, and modelling with the generated data is important for clinical applications and in understanding the underlying biological process.
The purpose of this special issue in the journal “CMES - Computer Modeling in Engineering and Sciences” aims to embrace the adoption, integration, and optimization of deep learning in medical signal analysis, providing the reader with an overview of this emerging technology and its unique applications and challenges in the domain of medical signal analysis.
Scopes (but are not limited to) the following:
• Theoretical understanding of deep learning in biomedical engineering;
• Transfer learning and multi-task learning;
• Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography);
• Joint semantic segmentation, object detection and scene recognition on biomedical images;
• Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming;
• Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.;
• Optimization by deep neural networks, Multi-dimensional deep learning;
• New model or new structure of convolutional neural network;
• Visualization and explainable deep neural network in medical signal analysis.
Published Papers
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Open Access
EDITORIAL
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Open Access
ARTICLE
MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks
Juhong Tie, Hui Peng, Jiliu Zhou
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 427-445, 2021, DOI:10.32604/cmes.2021.014107
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number…
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Open Access
ARTICLE
A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning
Dejia Shi, Hanzhong Zheng
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN). However, sometimes, patients’ treatment can…
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Open Access
ARTICLE
Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network
Dequan Guo, Qiao Yang, Yu-Dong Zhang, Tao Jiang, Hanbing Yan
CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 599-620, 2021, DOI:10.32604/cmes.2021.014119
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in
medical institutions. This matter arouses people’s attention. Traditional artificial waste classification is subjective
and cannot be put accurately; moreover, the working environment of sorting is poor and the efficiency is low.
Therefore, automated and effective sorting is needed. In view of the current development of deep learning, it can
provide a good auxiliary role for classification and realize automatic classification. In this paper, the ResNet-50
convolutional neural network based on the transfer learning method is applied to design the image…
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Open Access
ARTICLE
Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier
Xianqing Chen, Yan Yan
CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 325-343, 2021, DOI:10.32604/cmes.2021.014489
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Alcoholism is an unhealthy lifestyle associated with alcohol dependence. Not only does drinking for a long time leads to poor mental health and loss of self-control, but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs. Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health. As their drinking increases, they become dependent on alcohol and it affects their daily lives. Therefore, it is important…
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Open Access
ARTICLE
An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks
Xinxin Lu, Hong Zhang
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 281-297, 2020, DOI:10.32604/cmes.2020.010948
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and
practical application value. Emotion research based on the social network is
a relatively new topic in the field of psychology and medical health research.
The text emotion analysis of college students also has an important research
significance for the emotional state of students at a certain time or a certain
period, so as to understand their normal state, abnormal state and the reason
of state change from the information they wrote. In…
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Open Access
ARTICLE
Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification
Chaosheng Tang, Deepak Ranjan Nayak, Shuihua Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing,
misunderstanding, and communication problems. Therefore, it is necessary
to provide early diagnosis and timely treatment for HL. This study investigated
the advantages and disadvantages of three classical machine learning
methods: multilayer perceptron (MLP), support vector machine (SVM), and
least-square support vector machine (LS-SVM) approach and made a further
optimization of the LS-SVM model via wavelet entropy. The investigation
illustrated that themultilayer perceptron is a shallowneural network,while the
least square support vector machine uses hinge loss function…
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Open Access
REVIEW
Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review
Nazish Naheed, Muhammad Shaheen, Sajid Ali Khan, Mohammed Alawairdhi, Muhammad Attique Khan
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract In the area of pattern recognition and machine learning, features
play a key role in prediction. The famous applications of features are medical
imaging, image classification, and name a few more. With the exponential
growth of information investments in medical data repositories and health
service provision, medical institutions are collecting large volumes of data.
These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other
hand, this growth also made it difficult to comprehend and utilize data for
various purposes. The results of imaging data can become biased because of…
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Open Access
ARTICLE
A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism
Jiabin Wang, Kai Peng
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 345-363, 2020, DOI:10.32604/cmes.2020.011046
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract In many existing multi-view gait recognition methods based on images
or video sequences, gait sequences are usually used to superimpose and synthesize images and construct energy-like template. However, information may be lost
during the process of compositing image and capture EMG signals. Errors and the
recognition accuracy may be introduced and affected respectively by some factors
such as period detection. To better solve the problems, a multi-view gait recognition method using deep convolutional neural network and channel attention
mechanism is proposed. Firstly, the sliding time window method is used to capture EMG signals. Then, the back-propagation learning algorithm is used…
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Open Access
ARTICLE
PDNet: A Convolutional Neural Network Has Potential to be Deployed on Small Intelligent Devices for Arrhythmia Diagnosis
Fei Yang, Xiaoqing Zhang, Yong Zhu
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Heart arrhythmia is a group of irregular heartbeat conditions and
is usually detected by electrocardiograms (ECG) signals. Over the past years,
deep learning methods have been developed to classify different types of
heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off
between classification performance and parameters of deep learning methods.
To tackle this problem, this work proposes a convolutional neural network
(CNN) model named PDNet to recognize different types of heart arrhythmias
efficiently. In the PDNet, a convolutional block named PDblock is devised,
which is comprised of a pointwise convolutional layer…
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Open Access
ARTICLE
Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT
Liying Wang , Zhiqiang Xu, Shuihua Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1001-1015, 2020, DOI:10.32604/cmes.2020.010753
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Artificial Intelligence (AI) becomes one hotspot in the field of the medical images analysis and provides rather promising solution. Although some
research has been explored in smart diagnosis for the common diseases of urinary
system, some problems remain unsolved completely A nine-layer Convolutional
Neural Network (CNN) is proposed in this paper to classify the renal Computed
Tomography (CT) images. Four group of comparative experiments prove the
structure of this CNN is optimal and can achieve good performance with average
accuracy about 92.07 ± 1.67%. Although our renal CT data is not very large, we
do augment the training data by…
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Open Access
ARTICLE
A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products: An Application for Medical-Tech Classification
Inzamam Mashood Nasir, Muhammad Attique Khan, Majed Alhaisoni, Tanzila Saba, Amjad Rehman, Tassawar Iqbal
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1017-1033, 2020, DOI:10.32604/cmes.2020.010943
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Comic character detection is becoming an exciting and growing
research area in the domain of machine learning. In this regard, recently, many
methods are proposed to provide adequate performance. However, most of these
methods utilized the custom datasets, containing a few hundred images and fewer
classes, to evaluate the performances of their models without comparing it, with
some standard datasets. This article takes advantage of utilizing a standard publicly dataset taken from a competition, and proposes a generic data balancing
technique for imbalanced dataset to enhance and enable the in-depth training of
the CNN. In addition, to classify the superheroes…
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Open Access
ARTICLE
Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization
Zhuqing Jiao, Yixin Ji, Tingxuan Jiao, Shuihua Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 845-871, 2020, DOI:10.32604/cmes.2020.08999
（This article belongs to this Special Issue:
Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract Currently, functional connectomes constructed from neuroimaging data have
emerged as a powerful tool in identifying brain disorders. If one brain disease just manifests as some cognitive dysfunction, it means that the disease may affect some local connectivity in the brain functional network. That is, there are functional abnormalities in the
sub-network. Therefore, it is crucial to accurately identify them in pathological diagnosis.
To solve these problems, we proposed a sub-network extraction method based on graph
regularization nonnegative matrix factorization (GNMF). The dynamic functional networks
of normal subjects and early mild cognitive impairment (eMCI) subjects were vectorized
and the functional connection…
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