Recent Advances in Deep Learning for Image Analysis

Dear Colleagues,

Recently, deep learning algorithms have been used for a wide range of computer vision and image analysis tasks, such as image classification, graphics recognition, object detection, and image segmentation. However, deep learning still poses some challenges in regard to training time, network size, accuracy, computing power, and overfitting. These challenges need to be addressed in order to provide reliable and efficient deep learning networks. Hence, the aim of this Special Issue is to cover novel, optimized, high-performance, and hybrid deep-learning-based approaches for image analysis to address the aforementioned challenges in a variety of applications. Topics of interest include, but are not limited to, the following:

• Deep-learning-based image analysis in various disciplines (remote sensing, medicine, biology, etc.).
• Image analysis (classification, segmentation, recognition, and detection) using deep learning.
• Effective augmentation methods for deep-learning-based image analysis.
• Hybrid machine learning and deep learning methods for image analysis.
• Efficient deep learning architectures for image analysis.
• Deep learning models on mobile and embedded devices for image analysis.
• Transfer learning, domain adaptation, and knowledge distillation for image analysis.

Prof. Dr. Tan-Hsu Tan
Prof. Dr. Mohammad Alkhaleefah
Prof. Dr. Yang-Lang Chang
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-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.