Medical Image Processing and Analysis
This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Special Issue Information
Dear Colleagues,
Machine-learning-based approaches are attracting a lot of attention due to the wide range of applications in various fields. The last two decades have witnessed increasing interest in computer-aided medical systems for early detection, diagnosis, prognosis, risk assessment, and final therapy of diseases. The development of a reliable medical solution is a crucial task because there is no single standard approach covering all the subdomains, including data processing, regions of interest detection, image segmentation and registration, image fusion, and classification with high accuracy. Therefore, computer-aided diagnosis systems are still a highly challenging domain which provides enough space for improvement. These days, deep-learning-based methods are attracting much attention among researchers in the machine learning community due to their improved segmentation and classification results. Moreover, deep-learning-based methods have also lowered the barriers of data preprocessing and extreme set of users’ dependability. Consequently, the processing burden in medical imaging has now shifted from the human to the computer side, thus allowing more researchers to step into this well-regarded and momentous area. This leads to improved performance, both in terms of accuracy and decision time.
This Special Issue seeks high-quality research articles generally dealing with methods such as semantic segmentation and deep learning in the field of medical image processing. We are primarily interested in original research articles proposing novel solutions, covering new theories, and describing new implementations for medical image analytics.
Dr. Muhammad Attique Khan
Prof. Dr. Yu-Dong Zhang
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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.
Keywords
- deep-learning-based segmentation
- federated learning
- semantic segmentation for medical infection diagnosis
- wireless capsule endoscopy (WCE) imaging technology using deep learning
- magnetic resonance imaging (MRI)
- semantic techniques for MRI images
- FPGA with deep learning for medical imaging
- mammogram imaging modality using deep learning
- ultrasound imaging modality detection using deep learning
- X-ray computed tomography (CT)
- deep-learning-based CAD systems
- transfer learning in deep learning for medical imaging
- cancers classification using deep learning
- autoencoder-based features selection using deep learning in the medical field
- fusion of convolutional layers in deep learning for recognition
- optimal deep learning features selection for recognition
- fusion of image modality using deep learning
- deep-learning-based medical imaging retrieval
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Published Papers
Diagnostics - ISSN 2075-4418