Deep learning Methods for Medical Applications

  in Special Issue   Posted on June 15, 2019

Information for the Special Issue

Submission Deadline: Fri 30 Aug 2019
Journal Impact Factor : 4.383
Journal Name : Artificial Intelligence in Medicine
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

Any ailment in our organs can be visualized by using different modality signals and images, such as EEG, ECG, PCG, X-ray, magnetic resonance imaging, computerized tomography, Single photon emission computed tomography, Positron emission tomography, fundus and ultrasound images, etc., originating from various body parts to obtain useful information. Hospitals are encountering a massive influx of large multimodality patient data to be analysed accurately and with context understanding. Many machine learning algorithms have been developed to automatically detect the features that are characterizing the diseases depicted in medical images. Extracting the proper features from the medical images using advanced image or signal processing methods limits the amount of information available for the machine learning algorithm. Furthermore, feature selection is oftentimes subjective and it is not clear if two or more features report the same information. To overcome these problems, deep learning approaches implicitly learn these features from the training data and use them to support diagnosis and prognosis from medical images.

The deep learning techniques, like convolution neural networks (CNN), long short-term memory (LSTM), autoencoders, deep generative models and deep belief networks have already been applied to efficiently analyse possible large collections of data. Application of these methods to medical signals and images can aid the clinicians in clinical decision making. The special issue on “Deep learning methods for medical applications” calls for manuscripts on reports on new methods, approaches and application of deep learning. Manuscripts on explainable approaches that describe how deep learning models can help us interpret the data and explain the predictions are especially welcome.

Guest Editors

Dr.Victor Hugo Costa de Albuquerque

Universidade de Fortaleza, Centro de Ciências Tecnológicas.

Avenida Washington Soares, 1321

Edson Queiroz

60811341 – Fortaleza, CE – Brasil – Caixa-Postal: 60811341


Dr.Gustavo Ramirez (Managing Guest Editor)

Professor – University of Cauca



Dr.Dr. Oliver Faust

Senior Lecturer – Sheffield Hallam University

United Kingdom


Dr.Enas Abdulhay

Associate Professor – Department of Biomedical Engineering

Jordan University of Science and Technology


Dr.Blaz Zupan

University of Ljubljana

Faculty of Computer and Information Science



Submissions Open: 16 May, 2019

Submissions Due: 30 August 2019

Final Notification: March 2020


Authors who wish to submit to this special issue should do so using the link – by selecting \”VSI: Deep Learning\” in the article type field.

Closed Special Issues