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Deep Learning for Healthcare: Review, Opportunities and Challenges

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8366

Special Issue Editors


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Guest Editor
Department of Electronic Engineering, University of Rome “Tor Vergata”, 00133 Rome, Italy
Interests: data privacy; machine learning; distributed systems

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Guest Editor
Department of Electrical Engineering, Tor Vergata University of Rome, Via del Politecnico, 1, 00133 Roma, Italy
Interests: wireless and mobile networks; framework design; analytic modeling; performance evaluation through simulation and test-bedding

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Guest Editor
Dept. of Enterprise Engineering, Università degli Studi di Roma "Tor Vergata", Rome, Italy
Interests: natural language processing; machine learning; information retrieval; lexical acquisition; representation of word and phrase meaning

Special Issue Information

Dear Colleagues,

Deep learning is revolutionizing many industrial processes and bringing innovation across different sectors, among which healthcare is one of the most impacted. It is a key enabler for data-driven medicine, evidence-based medicine, and precision medicine, which all the experts recognize as the future of healthcare.

The growing amount of data coming from thousands of sensors and medical devices or digitalized medical documentations will further enable an unprecedented explosion of deep learning techniques able to reduce and to optimize the data analysis cost, providing more accurate and personalized treatments.

Deep learning has already been widely demonstrated and successfully applied in the field of healthcare image recognition, but the applications have a wider target, and the contribution strongly impacts several other fields of medicine.

Intelligent systems can help doctors and healthcare professionals, complementing human skills with the unrivaled machine learning ability to process huge amounts of data going toward evidence-based medicine and precision medicine.

We are living in a time where such a revolution has just begun, and for this reason, we solicit innovative works in the field. Topics of interest include (but are not limited to):

  • Applications of deep learning for medicine, human biology, and healthcare;
  • Applications of deep learning for clinical decision making;
  • Applications of deep learning for processing medical data from sensors;
  • Computational medicine;
  • Deep learning for the automatic processing of medical documentations;
  • Learning on healthcare big data;
  • Explainability of AI for healthcare;
  • Privacy and security on the previous systems.

We aim to bring together researchers of several application fields, including software engineering, healthcare engineering, electronics, data science, statistics, and mathematics.

Dr. Lorenzo Bracciale
Dr. Pierpaolo Loreti
Dr. Danilo Croce
Guest Editors

Manuscript Submission Information

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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.

Published Papers (2 papers)

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Research

14 pages, 8794 KiB  
Article
Classification of Skin Cancer Lesions Using Explainable Deep Learning
by Muhammad Zia Ur Rehman, Fawad Ahmed, Suliman A. Alsuhibany, Sajjad Shaukat Jamal, Muhammad Zulfiqar Ali and Jawad Ahmad
Sensors 2022, 22(18), 6915; https://doi.org/10.3390/s22186915 - 13 Sep 2022
Cited by 18 | Viewed by 5519
Abstract
Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained [...] Read more.
Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained popularity due to their effectiveness and efficiency. These systems can assist dermatologists in the early detection of skin cancer, which can be lifesaving. In this paper, the pre-trained MobileNetV2 and DenseNet201 deep learning models are modified by adding additional convolution layers to effectively detect skin cancer. Specifically, for both models, the modification includes stacking three convolutional layers at the end of both the models. A thorough comparison proves that the modified models show their superiority over the original pre-trained MobileNetV2 and DenseNet201 models. The proposed method can detect both benign and malignant classes. The results indicate that the proposed Modified DenseNet201 model achieves 95.50% accuracy and state-of-the-art performance when compared with other techniques present in the literature. In addition, the sensitivity and specificity of the Modified DenseNet201 model are 93.96% and 97.03%, respectively. Full article
(This article belongs to the Special Issue Deep Learning for Healthcare: Review, Opportunities and Challenges)
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15 pages, 24218 KiB  
Article
Numerical Approach to Facial Palsy Using a Novel Registration Method with 3D Facial Landmark
by Junsik Kim, Hyungwha Jeong, Jeongmok Cho, Changsik Pak, Tae Suk Oh, Joon Pio Hong, Soonchul Kwon and Jisang Yoo
Sensors 2022, 22(17), 6636; https://doi.org/10.3390/s22176636 - 2 Sep 2022
Cited by 3 | Viewed by 2087
Abstract
Treatment of facial palsy is essential because neglecting this disorder can lead to serious sequelae and further damage. For an objective evaluation and consistent rehabilitation training program of facial palsy patients, a clinician’s evaluation must be simultaneously performed alongside quantitative evaluation. Recent research [...] Read more.
Treatment of facial palsy is essential because neglecting this disorder can lead to serious sequelae and further damage. For an objective evaluation and consistent rehabilitation training program of facial palsy patients, a clinician’s evaluation must be simultaneously performed alongside quantitative evaluation. Recent research has evaluated facial palsy using 68 facial landmarks as features. However, facial palsy has numerous features, whereas existing studies use relatively few landmarks; moreover, they do not confirm the degree of improvement in the patient. In addition, as the face of a normal person is not perfectly symmetrical, it must be compared with previous images taken at a different time. Therefore, we introduce three methods to numerically approach measuring the degree of facial palsy after extracting 478 3D facial landmarks from 2D RGB images taken at different times. The proposed numerical approach performs registration to compare the same facial palsy patients at different times. We scale landmarks by performing scale matching before global registration. After scale matching, coarse registration is performed with global registration. Point-to-plane ICP is performed using the transformation matrix obtained from global registration as the initial matrix. After registration, the distance symmetry, angular symmetry, and amount of landmark movement are calculated for the left and right sides of the face. The degree of facial palsy at a certain point in time can be approached numerically and can be compared with the degree of palsy at other times. For the same facial expressions, the degree of facial palsy at different times can be measured through distance and angle symmetry. For different facial expressions, the simultaneous degree of facial palsy in the left and right sides can be compared through the amount of landmark movement. Through experiments, the proposed method was tested using the facial palsy patient database at different times. The experiments involved clinicians and confirmed that using the proposed numerical approach can help assess the progression of facial palsy. Full article
(This article belongs to the Special Issue Deep Learning for Healthcare: Review, Opportunities and Challenges)
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