Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Emerging Trends of Deep Learning in Medical Imaging: Challenges and...
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies

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 (20 December 2021) | Viewed by 13677

Special Issue Editors

Dr. Fahmi Khalifa

E-Mail Website1 Website2 Website3
Guest Editor
Assistant Professor, Mansoura University, Egypt and Postdoctoral associate, University of Louisville, USA
Interests: medical signal/image processing; machine learning; deep learning; pattern recognition; image segmentation; image/shape registration; image encryption
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Shalaby

E-Mail Website1 Website2
Guest Editor
University of Louisville, USA
Interests: computer vision; image processing; medical imaging; bioengineering
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Soliman

E-Mail Website
Guest Editor
Research Scientist, University of Louisville, Louisville, KY, USA
Interests: signal processing; probability; random variables; stochastic processes and pattern recognition; image segmentation and image/shape registration; stereo reconstruction; image formation; face detection; face recognition; object tracking and shape-from-shading

Special Issue Information

Dear Colleagues,

The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of medical image analysis to examine the structure and assess the function of human organs and/or to provide early prediction and assessment of diseases. Conventional machine learning methods have been the focus of intense investigation for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs to deal with complexities of medical data by radically changing research methodologies toward a data-oriented approach. This Special Issue calls for recent studies and research work focusing on deep learning applications for medical image analysis. Papers of both theoretical and applicative nature are welcome, as well as high-quality review and survey papers for the medical image analysis research community. Major topics of interest include but are not restricted to the following:

  • Artificial intelligence paradigms for medical image, processing, analysis, and diagnosis;
  • Novel deep learning architectures for efficient feature extraction and classification;
  • Optimization techniques for DL architectures that focus on medical image processing/analysis;
  • Unsupervised and/or semi-supervised DL approaches to deal with small or poorly annotated medical data sets;
  • Augmented and hybrid learning tools, e.g., generative adversarial network (GAN), Bayesian GANs, Neural style transfer, etc.;
  • Multi-level fusion techniques that can maximize the benefits of integrating multiple/complex data sources with different imaging modalities;
  • Deep learning application for big medical data;
  • Transfer learning techniques for medical data.

Dr. Fahmi Khalifa
Dr. Ahmed Shalaby
Dr. Ahmed Soliman
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

  • deep learning
  • artificial intelligence
  • big data
  • medical imaging analysis
  • optimization
  • data augmentation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 1857 KiB  
Article
Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
by Abeer A. AbdElhamid, Eman AbdElhalim, Mohamed A. Mohamed and Fahmi Khalifa
Appl. Sci. 2022, 12(4), 2080; https://doi.org/10.3390/app12042080 - 17 Feb 2022
Cited by 16 | Viewed by 3440
Abstract
Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been [...] Read more.
Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate. Full article
(This article belongs to the Special Issue Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies)
►▼ Show Figures

Figure 1

16 pages, 1236 KiB  
Article
Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines
by Falko Lavitt, Demi J. Rijlaarsdam, Dennet van der Linden, Ewelina Weglarz-Tomczak and Jakub M. Tomczak
Appl. Sci. 2021, 11(11), 4912; https://doi.org/10.3390/app11114912 - 27 May 2021
Cited by 23 | Viewed by 9016
Abstract
In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell [...] Read more.
In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features). Full article
(This article belongs to the Special Issue Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop