Deep Learning and Machine Learning in Biomedical Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 April 2024 | Viewed by 17711

Special Issue Editors

Department of Translational Biomedical Sciences, College of Medicine, Dong-A University, Busan 49201, Republic of Korea
Interests: brain; diseases; image classification; medical image processing; neurophysiology; positron emission tomography; biomedical MRI; cognition; computerised tomography; feature extraction; image segmentation; neural nets; unsupervised learning
Special Issues, Collections and Topics in MDPI journals
Department of Management and Information Systems, Dong-A University, Busan 49315, Republic of Korea
Interests: data mining; machine learning; deep learning; statistical analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The adoption of machine learning and deep learning analytics in the bio-medical field is progressing at a rapid pace, with some applications already used in pre-clinical and clinical settings. In addition, various types of bio-medical data continue to be used, and the CNN, RNN, and transformer technologies used of analysis continue to be further developed. There are many types of bio-medical data, such as image data, pathological tissue data, waveform data, natural language data, genetic data, and voice data, etc.

In this Special Issue, we aim to collect the current research on the latest machine learning and deep learning techniques of various bio-medical data. Additionally, a hypothesis for a fusion analysis technique of various bio-medical data would be most welcome.

Dr. Do-Young Kang
Dr. Sangjin Kim
Prof. Dr. Hyuntae Park
Guest Editors

Manuscript Submission Information

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Keywords

  • imaging: X-ray, US, CT, MRI, fMRI, PET, SPECT, molecular imaging, pathologic slice, cell imaging, etc
  • text: EMR data, descriptive data, etc
  • waveform: EEG, MEG, ECoG, ECG, voice, electrophisiologic data, etc
  • genetic data

Published Papers (9 papers)

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Research

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18 pages, 3690 KiB  
Article
Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model
by Ashwag Albakri and Yahya Muhammed Alqahtani
Appl. Sci. 2023, 13(10), 6108; https://doi.org/10.3390/app13106108 - 16 May 2023
Cited by 4 | Viewed by 1514
Abstract
The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store [...] Read more.
The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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14 pages, 3124 KiB  
Article
Improvement of Auxiliary Diagnosis of Diabetic Cardiovascular Disease Based on Data Oversampling and Deep Learning
by Weiming Yang, Yujia Guo and Yuliang Liu
Appl. Sci. 2023, 13(9), 5449; https://doi.org/10.3390/app13095449 - 27 Apr 2023
Viewed by 967
Abstract
Diabetic cardiovascular disease is a common complication of diabetes, which can lead to high-mortality diseases such as diabetic cardiomyopathy and atherosclerosis in serious cases. Therefore, effective prevention and management of diabetic cardiovascular disease is demanded. Clinical medical data officers are faced with a [...] Read more.
Diabetic cardiovascular disease is a common complication of diabetes, which can lead to high-mortality diseases such as diabetic cardiomyopathy and atherosclerosis in serious cases. Therefore, effective prevention and management of diabetic cardiovascular disease is demanded. Clinical medical data officers are faced with a situation of a small amount of data and uneven data distribution. In this paper, we propose data oversampling synthesis techniques based on weight and extension algorithms. It can combine 1D-convolutional neural networks and long short-term memory neural networks to solve the problem of a lack of original data. First of all, a few samples based on feature weight are synthesized to make the original unbalanced data evenly distributed. Secondly, the original data are extended and corrected to expand the number of samples. Finally, the deep learning algorithm is used to extract features and classify whether the data have diabetic cardiovascular disease. Data synthesis based on weight and extension algorithms was evaluated on the actual medical datasets and obtained an accuracy of 93.53% and specificity of 94.37%, which confirms that it is an improved solution compared to the other algorithms. Hence, this paper contributes not only a substantial saving of human resources but also improves the efficiency of the clinical diagnosis of diabetic cardiovascular disease, which is conducive to the early detection and treatment of diseases. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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14 pages, 1863 KiB  
Article
Vision Transformer Approach for Classification of Alzheimer’s Disease Using 18F-Florbetaben Brain Images
by Hyunji Shin, Soomin Jeon, Youngsoo Seol, Sangjin Kim and Doyoung Kang
Appl. Sci. 2023, 13(6), 3453; https://doi.org/10.3390/app13063453 - 08 Mar 2023
Cited by 4 | Viewed by 2845
Abstract
Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose [...] Read more.
Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images. The convolutional neural network (CNN) is a representative application of deep learning, serving as a powerful tool for the diagnosis of AD. Recently, vision transformers (ViT) have yielded classification performance exceeding that of CNN in some diagnostic image classifications. Because the brain is a very complex network with interrelated regions, ViT, which captures direct relationships between images, may be more effective for brain image analysis than CNN. Therefore, we propose a method for classifying dementia images by applying 18F-Florbetaben positron emission tomography (PET) images to ViT. Data were evaluated via binary (normal control and abnormal) and ternary (healthy control, mild cognitive impairment, and AD) classification. In a performance comparison with the CNN, VGG19 was selected as the comparison model. Consequently, ViT yielded more effective performance than VGG19 in binary classification. However, in ternary classification, the performance of ViT cannot be considered excellent. These results show that it is hard to argue that the ViT model is better at AD classification than the CNN model. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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33 pages, 16386 KiB  
Article
The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm
by Nikola Anđelić, Ivan Lorencin, Sandi Baressi Šegota and Zlatan Car
Appl. Sci. 2023, 13(1), 574; https://doi.org/10.3390/app13010574 - 31 Dec 2022
Cited by 2 | Viewed by 1248
Abstract
Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be [...] Read more.
Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient’s blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1score¯±SD(F1score) equal to 0.99±5.8×103, 0.99±5.4×103, 0.998±1.3×103, 0.98±1.19×103, and 0.99±5.39×103, respectively. For the multiclass problem, OneVsRestClassifer was used in combination with GPSC 5-fold cross-validation and random hyperparameter search, and the best case was achieved with a dataset balanced with the Borderline SMOTE method. To evaluate symbolic expressions obtained in this case previous evaluation metric methods were used however for AUC, Precision, Recall, and F1score the macro values were computed since this method calculates metrics for each label, and find their unweighted mean value. In multiclass case the ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1score¯macro±SD(F1score) are equal to 0.934±9×103, 0.987±1.8×103, 0.942±6.9×103, 0.934±7.84×103 and 0.932±8.4×103, respectively. For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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20 pages, 1416 KiB  
Article
Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
by Mohammed Isam Al-Hiyali, Norashikin Yahya, Ibrahima Faye, Maged S. Al-Quraishi and Abdulhakim Al-Ezzi
Appl. Sci. 2022, 12(18), 9339; https://doi.org/10.3390/app12189339 - 18 Sep 2022
Cited by 4 | Viewed by 1694
Abstract
The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of [...] Read more.
The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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15 pages, 947 KiB  
Article
Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm
by Soojeong Lee, Hyeonjoon Moon, Chang-Hwan Son and Gangseong Lee
Appl. Sci. 2022, 12(16), 8355; https://doi.org/10.3390/app12168355 - 21 Aug 2022
Cited by 3 | Viewed by 1507
Abstract
Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the [...] Read more.
Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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15 pages, 3019 KiB  
Article
Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning
by Hyun-Ji Shin, Hyemin Yoon, Sangjin Kim and Do-Young Kang
Appl. Sci. 2022, 12(15), 7355; https://doi.org/10.3390/app12157355 - 22 Jul 2022
Viewed by 1301
Abstract
18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to [...] Read more.
18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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17 pages, 4421 KiB  
Article
Skin Lesion Segmentation Based on Vision Transformers and Convolutional Neural Networks—A Comparative Study
by Yonis Gulzar and Sumeer Ahmad Khan
Appl. Sci. 2022, 12(12), 5990; https://doi.org/10.3390/app12125990 - 12 Jun 2022
Cited by 46 | Viewed by 3698
Abstract
Melanoma skin cancer is considered as one of the most common diseases in the world. Detecting such diseases at early stage is important to saving lives. During medical examinations, it is not an easy task to visually inspect such lesions, as there are [...] Read more.
Melanoma skin cancer is considered as one of the most common diseases in the world. Detecting such diseases at early stage is important to saving lives. During medical examinations, it is not an easy task to visually inspect such lesions, as there are similarities between lesions. Technological advances in the form of deep learning methods have been used for diagnosing skin lesions. Over the last decade, deep learning, especially CNN (convolutional neural networks), has been found one of the promising methods to achieve state-of-art results in a variety of medical imaging applications. However, ConvNets’ capabilities are considered limited due to the lack of understanding of long-range spatial relations in images. The recently proposed Vision Transformer (ViT) for image classification employs a purely self-attention-based model that learns long-range spatial relations to focus on the image’s relevant parts. To achieve better performance, existing transformer-based network architectures require large-scale datasets. However, because medical imaging datasets are small, applying pure transformers to medical image analysis is difficult. ViT emphasizes the low-resolution features, claiming that the successive downsampling results in a lack of detailed localization information, rendering it unsuitable for skin lesion image classification. To improve the recovery of detailed localization information, several ViT-based image segmentation methods have recently been combined with ConvNets in the natural image domain. This study provides a comprehensive comparative study of U-Net and attention-based methods for skin lesion image segmentation, which will assist in the diagnosis of skin lesions. The results show that the hybrid TransUNet, with an accuracy of 92.11% and dice coefficient of 89.84%, outperforms other benchmarking methods. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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Review

Jump to: Research

32 pages, 2754 KiB  
Review
Review of Quantitative Methods for the Detection of Alzheimer’s Disease with Positron Emission Tomography
by Jarrad Perron and Ji Hyun Ko
Appl. Sci. 2022, 12(22), 11463; https://doi.org/10.3390/app122211463 - 11 Nov 2022
Cited by 2 | Viewed by 1649
Abstract
The dementia spectrum is a broad range of disorders with complex diagnosis, pathophysiology, and a limited set of treatment options, where the most common variety is Alzheimer’s disease (AD). Positron emission tomography (PET) has become a valuable tool for the detection of AD; [...] Read more.
The dementia spectrum is a broad range of disorders with complex diagnosis, pathophysiology, and a limited set of treatment options, where the most common variety is Alzheimer’s disease (AD). Positron emission tomography (PET) has become a valuable tool for the detection of AD; however, following the results of post-mortem studies, AD diagnosis has modest sensitivity and specificity at best. It remains common practice that readings of these images are performed by a physician’s subjective impressions of the spatial pattern of tracer uptake, and so quantitative methods based on established biomarkers have had little penetration into clinical practice. The present study is a review of the data-driven methods available for molecular neuroimaging studies (fluorodeoxyglucose-/amyloid-/tau-PET), with emphasis on the use of machine/deep learning as quantitative tools complementing the specialist in detecting AD. This work is divided into two broad parts. The first covers the epidemiology and pathology of AD, followed by a review of the role of PET imaging and tracers for AD detection. The second presents quantitative methods used in the literature for detecting AD, including the general linear model and statistical parametric mapping, 3D stereotactic surface projection, principal component analysis, scaled subprofile modeling, support vector machines, and neural networks. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Data)
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