Special Issue "Advances of Machine and Deep Learning in the Health Domain"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Dr. Antonio Celesti
E-Mail Website1 Website2
Guest Editor
MIFT Department, University of Messina, Viale F. Stagno d'Alcontres, 31 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
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Dr. Ivanoe De Falco
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking of National Research Council (ICAR-CNR), 80131 Naples, Italy
Interests: artificial intelligence; machine learning; soft computing; computational intelligence; parallel and distributed computing; explainable artificial intelligence; AI/ML applications to eHealth and mobile health; pattern recognition; signal processing; optimization; classification; regression; time series forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Antonino Galletta
E-Mail Website
Guest Editor
MIFT Department, University of Messina, 98166 Messina, Italy
Interests: big data; cloud computing; osmotic computing; e-health
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Dr. Giovanna Sannino
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking – National Research Council of Italy (ICAR-CNR), 80131 Naples, Italy
Interests: eHealth; mobile health; signal processing; pattern recognition; biomechanical and physiological parameter extraction and analysis; statistical analysis; machine learning/artificial intelligence techniques for eHealth applications; ICT-based intelligent solutions for chronic disease (cardiovascular diseases); wearable devices (ECG sensors, accelerometer sensors, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 1st edition of the IEEE International Conference on ICT Solutions for eHealth (ICTS4eHealth) will be held on 5–8 September 2021 in Athens (Greece) in conjunction with the 26th IEEE Symposium on Computers and Communications (ISCC).

For more information about the conference, please use this link:

https://www.icts4ehealth.icar.cnr.it/

Machine and Deep Learning deal with data, and one of their goals is to extract information and related knowledge that is hidden in them in order to make detections and/or predictions and, subsequently, take decisions. With the terms “Machine and Deep Learning”, we cover a wide range of theories, methods, algorithms, and architectures that are used to this end.

This Special Issue will cover promising developments in the related areas of machine and deep learning applied to the health domain and offer possible paths for the future.

The authors of selected papers that are presented at the International IEEE ICTS4eHealth Conference 2021 are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together in this Special Issue’s website. Accepted extended papers will be free of charge. There are no page limitations for this journal.

We are also inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in artificial intelligence in the health domain.

The main topics include but are not limited to:

  • Knowledge management of health data;
  • Data mining and knowledge discovery in healthcare;
  • Machine and deep learning approaches for health data;
  • Explainable ai models for health, biology, and medicine;
  • Decision support systems for healthcare and wellbeing;
  • AI for precision medicine;
  • Optimization for healthcare problems;
  • Regression and forecasting for medical and/or biomedical signals;
  • Healthcare information systems;
  • Wellness information systems;
  • Medical signal and image processing and techniques;
  • Medical expert systems;
  • Diagnoses and therapy support systems;
  • Biomedical applications;
  • Applications of AI in healthcare and wellbeing systems;
  • machine learning-based medical systems;
  • medical data and knowledge bases;
  • neural networks in medicine;
  • ambient intelligence and pervasive computing in medicine and healthcare;
  • AI in genomics;
  • AI for healthcare social networks.

Dr. Antonio Celesti
Dr. Ivanoe De Falco
Dr. Antonino Galletta
Dr. Giovanna Sannino
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 papers will be 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. Computers is an international peer-reviewed open access monthly 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 1400 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.

Published Papers (5 papers)

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Research

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Article
Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches
Computers 2022, 11(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/computers11010010 - 07 Jan 2022
Cited by 1 | Viewed by 223
Abstract
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a [...] Read more.
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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Article
Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier
Computers 2022, 11(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/computers11010008 - 04 Jan 2022
Viewed by 157
Abstract
Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The [...] Read more.
Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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Article
Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Computers 2021, 10(11), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110139 - 28 Oct 2021
Cited by 1 | Viewed by 585
Abstract
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important [...] Read more.
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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Article
B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
Computers 2021, 10(11), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/computers10110136 - 25 Oct 2021
Cited by 8 | Viewed by 694
Abstract
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select [...] Read more.
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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Review

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Review
A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention
Computers 2022, 11(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/computers11010006 - 31 Dec 2021
Viewed by 161
Abstract
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and [...] Read more.
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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