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Applied Sciences
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Medical Imaging Using Machine Learning and Deep Learning
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Medical Imaging Using Machine Learning and Deep Learning

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 21684

Special Issue Editors

Dr. Rui Guo

E-Mail Website
Guest Editor
Beijing National Research Center for Information Science and Technology (BNRist), Institute for Precision Medicine, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: deep learning; artificial intelligence; machine learning; multiphysical data inversion
Dr. He-Ming Yao

E-Mail Website
Guest Editor
Department of Mathematics, The University of Hong Kong, Hong Kong, China
Interests: computational science; AI and machine learning; nanomaterials; electromagnetics
Dr. Francesco Zardi

E-Mail Website
Guest Editor
ELEDIA Research Center (ELEDIA@UniTN), Department of Civil, Environmental, and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Interests: microwave imaging; inverse scattering; smart electromagnetic environment; clustered array architectures
Dr. Mengchu Wang

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, Via Eudossiana, Rome, Italy
Interests: microwave imaging; inverse scattering; antenna design; dielectric properties’ measurement

Special Issue Information

Dear Colleagues,

Recent years have witnessed a rapid growth of interest in the development of intelligent imaging systems for medical purposes. Intelligent medical imaging is attractive for its high speed, super-resolution, and low cost. In particular, machine learning (ML) and deep learning (DL) techniques that seamlessly integrate big data and high-performance computing have largely facilitated the study of advanced medical imaging systems and their applications. So far, a wide range of work has shown the merit of ML/DL-based imaging systems as compared to conventional ones. Still, a considerable amount of challenges remain to be addressed in this field, concerning not only fundamental theory but also its clinical applications.

This Special Issue will be dedicated to intelligent medical imaging pipelines, including but not limited to the learning theory, smart system design, imaging methods, algorithms, signal and image processing techniques, with their applications to electromagnetic imaging/computed tomography (CT)/magnetic resonance imaging (MRI)/positron emission tomography (PET)/ultrasound (US), as well as multimodalities joint imaging.

Dr. Rui Guo
Dr. He-Ming Yao
Dr. Francesco Zardi
Dr. Mengchu Wang
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. Applied Sciences 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 2400 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

  • machine learning
  • deep learning
  • medical imaging
  • imaging methods
  • smart systems

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Published Papers (2 papers)

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Research

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15 pages, 2585 KiB  
Article
Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals
by Talha Iqbal, Adnan Elahi, William Wijns, Bilal Amin and Atif Shahzad
Appl. Sci. 2023, 13(5), 2950; https://doi.org/10.3390/app13052950 - 24 Feb 2023
Cited by 13 | Viewed by 2671
Abstract
Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from [...] Read more.
Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature’s 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. Full article
(This article belongs to the Special Issue Medical Imaging Using Machine Learning and Deep Learning)
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Review

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25 pages, 4918 KiB  
Review
Applying Deep Learning to Medical Imaging: A Review
by Huanhuan Zhang and Yufei Qie
Appl. Sci. 2023, 13(18), 10521; https://doi.org/10.3390/app131810521 - 21 Sep 2023
Cited by 10 | Viewed by 18150
Abstract
Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of [...] Read more.
Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of diseases and how it has revolutionized the medical imaging field. Furthermore, we examine the most recent DL techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in medical imaging. Lastly, we provide insights into the future of DL in medical imaging, highlighting its potential advancements and challenges. Full article
(This article belongs to the Special Issue Medical Imaging Using Machine Learning and Deep Learning)
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