Special Issue "Information-Centered Healthcare"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (18 December 2017)

Special Issue Editors

Guest Editor
Prof. Adel S. Elmaghraby

Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY 40292, USA
Website | E-Mail
Interests: Network and Information forensics; Biomedical Imaging; Multimedia and Virtual Reality Systems; Artificial Intelligence; Performance Evaluation; Computer Modeling and Simulation; Human-Machine Systems; Logistics; Automation and Manufacturing; Distributed Systems; Bioinformatics applications
Guest Editor
Dr. Daniel Sierra-Sosa

University of Louisville, Kentucky, USA
Website | E-Mail
Interests: Image and Signal Processing; Computer Imaging
Guest Editor
Prof. Begoña Garcia-Zapirain

Head of DeustoTech LIFE Unit, University of Deusto, Bilbao, Spain
Website | E-Mail
Phone: +34619967223
Interests: Signal processing; Medical signal processing algorithms; Software for helping diagnosis; (tele-)treatment and (tele-)assistance; Otolaryngology; Dermatology; Radiology; Oncology; Functional Magnetic Resonance Scanning; Games for therapy

Special Issue Information

Dear Colleagues,

The IEEE Symposium in Signal Processing and Information 2017 will be held over the period 18–20 December in Bilbao, Spain. For seventeen years this series of international symposia has congregated researchers from around the world, covering topics that relate to signal processing and information technology in a wide range of applications. Given the extremely rapid pace of development in information technologies applied to healthcare, in this year’s Symposium we will discuss topics related with information-centered healthcare. With this Special Issue we look forward to summarize remarkable contributions made by the academic community attending to the Symposium in health analytics, technology for active aging, mobile-health, signal processing for healthcare, integrated cancer, medical imaging and games for health and wellbeing.

Prof. Adel Elmaghraby
Dr. Daniel Sierra-Sosa
Prof. Begoña Garcia-Zapirain
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. Information is an international peer-reviewed open access quarterly 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 850 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.


  • health analytics
  • information technologies for health
  • mobile-health
  • medical signal processing
  • medical imaging processing
  • active aging and wellbeing

Published Papers (1 paper)

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Open AccessArticle Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network
Information 2018, 9(1), 19; doi:10.3390/info9010019
Received: 18 December 2017 / Revised: 7 January 2018 / Accepted: 12 January 2018 / Published: 16 January 2018
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Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes
[...] Read more.
Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively. Full article
(This article belongs to the Special Issue Information-Centered Healthcare)

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