sensors-logo

Journal Browser

Journal Browser

Deep Learning and Big Data for Healthcare and Industry (Industry 5.0)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 16082

Special Issue Editors


E-Mail Website
Guest Editor
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: data science; machine learning; data structures and algorithms; systems engineering; neural networks; data mining; project management; tensor flow; predictive modelling; artificial intelligence; hadoop; apache spark; software development; empirical researchbig data
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Interests: computational intellgence; neural networks; image processing; expert systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Interests: database usability; advanced data analytics; graph data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning, also called deep structured learning or hierarchical learning, is an essential member of the family's machine learning method. It allows computational models composed of multiple processing layers to be fed with raw data and automatically learn various abstract representations of data for detection and classification.

Healthcare is now an open field to get advantageous use of deep learning. Big data advances and challenges are open to provide systems that can be accurate enough to be useful to the clinician and the patient in the health itinerary. Industrial data is also massive and can be efficiently and effectively utilized for the industry's early warning system.

The goal of this Special Issue is to put together relevant contributions of deep learning and big data applications in healthcare and industry (Industry 5.0). On the one hand, the applications can include works with medical images (magnetic resonance, radioscopy, and tomography, echography, nuclear medicine), contributions to signal processing (cardiac, neural, long-term monitoring, wellness devices), data from large forms (primary attention, specialized medicine, clinical practice, electronic health recordings, hospital information systems, interoperability), or industrial data for an early warning system in assembly lines.

This Special Issue will bring together researchers from diverse fields and specialization, such as healthcare engineering, bioinformatics, medical doctors, computer engineering, computer science, information technology, statistics, production engineering, operations management, manufacturing, and mathematics. To sum up, feature interpretation remains an open issue in deep learning and the state of the art of big data. Still, it takes particular relevance in healthcare and industry applications, so contributions, including insights into this hot and open topic, are welcomed.

Dr. Muhammad Fazal Ijaz
Prof. Dr. Marcin Woźniak
Dr. Imran Razzak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Deep learning architectures and training procedures
  • Big data safety and security
  • Computer-aided diagnosis
  • Image guided therapy for personalized medicine
  • Personalized medicine (System, Algorithms, IoT Architecture)
  • Automated intelligent manufacturing for Industry 5.0
  • Data-driven health systems
  • Biomedical data (Images, Signal, Digital information, etc.)
  • Feature extraction and interpretation
  • Electronic health recording (Devices, Sensors, etc.)
  • Nano science for healthcare
  • Early warning system and fault detection

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 4029 KiB  
Article
A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI
by Mirza Mumtaz Zahoor, Shahzad Ahmad Qureshi, Sameena Bibi, Saddam Hussain Khan, Asifullah Khan, Usman Ghafoor and Muhammad Raheel Bhutta
Sensors 2022, 22(7), 2726; https://doi.org/10.3390/s22072726 - 01 Apr 2022
Cited by 40 | Viewed by 3857
Abstract
Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is [...] Read more.
Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset. Full article
(This article belongs to the Special Issue Deep Learning and Big Data for Healthcare and Industry (Industry 5.0))
Show Figures

Figure 1

17 pages, 2506 KiB  
Article
A Fuzzy Rule-Based System for Classification of Diabetes
by Khalid Mahmood Aamir, Laiba Sarfraz, Muhammad Ramzan, Muhammad Bilal, Jana Shafi and Muhammad Attique
Sensors 2021, 21(23), 8095; https://doi.org/10.3390/s21238095 - 03 Dec 2021
Cited by 22 | Viewed by 3364
Abstract
Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have [...] Read more.
Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes. Full article
(This article belongs to the Special Issue Deep Learning and Big Data for Healthcare and Industry (Industry 5.0))
Show Figures

Figure 1

24 pages, 2015 KiB  
Article
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
by Moumita Mandal, Pawan Kumar Singh, Muhammad Fazal Ijaz, Jana Shafi and Ram Sarkar
Sensors 2021, 21(16), 5571; https://doi.org/10.3390/s21165571 - 18 Aug 2021
Cited by 71 | Viewed by 3406
Abstract
In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason [...] Read more.
In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost. Full article
(This article belongs to the Special Issue Deep Learning and Big Data for Healthcare and Industry (Industry 5.0))
Show Figures

Figure 1

19 pages, 913 KiB  
Article
Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals
by Hamid Mukhtar, Saeed Mian Qaisar and Atef Zaguia
Sensors 2021, 21(16), 5456; https://doi.org/10.3390/s21165456 - 13 Aug 2021
Cited by 24 | Viewed by 3679
Abstract
Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a [...] Read more.
Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset. Full article
(This article belongs to the Special Issue Deep Learning and Big Data for Healthcare and Industry (Industry 5.0))
Show Figures

Figure 1

Back to TopTop