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Electronics
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Deep Learning for Computer Vision, 2nd Edition
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Deep Learning for Computer Vision, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 555

Special Issue Editors

Dr. Mingliang Gao

E-Mail Website
Guest Editor
College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China
Interests: computer vision; machine learning; intelligent optimization control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jing-Ming Guo

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: machine (deep) learning; neural network; multimedia; image processing; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to cover a wide range of deep learning applications within the computer vision domain, including image processing, pattern recognition, machine learning, and artificial intelligence techniques applied to visual data analysis. It highlights innovative methodologies and algorithms for tasks such as image classification, object detection and tracking, semantic segmentation, scene understanding, and more. Additionally, we invite contributions that explore the integration of computer vision with emerging technologies like augmented reality, virtual reality, and robotics, as well as applications in diverse sectors such as healthcare, transportation, surveillance, entertainment, and manufacturing. By offering a platform for cutting-edge research and practical applications, this Special Issue seeks to promote knowledge sharing, encourage interdisciplinary collaborations, and drive advancements in computer vision technologies to effectively address real-world challenges.

Dr. Mingliang Gao
Prof. Dr. Jing-Ming Guo
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. Electronics 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
  • object detection
  • image generation
  • super resolution
  • deep learning theory
  • attention network
  • image enhancement
  • artificial intelligence

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Published Papers (1 paper)

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Research

15 pages, 7826 KiB  
Article
Tongue Image Segmentation and Constitution Identification with Deep Learning
by Chien-Ho Lin, Sien-Hung Yang and Jiann-Der Lee
Electronics 2025, 14(4), 733; https://doi.org/10.3390/electronics14040733 - 13 Feb 2025
Viewed by 428
Abstract
Traditional Chinese medicine (TCM) gathers patient information through inspection, olfaction, inquiry, and palpation, analyzing and interpreting the data to make a diagnosis and offer appropriate treatment. Traditionally, the interpretation of this information relies heavily on the physician’s personal knowledge and experience. However, diagnostic [...] Read more.
Traditional Chinese medicine (TCM) gathers patient information through inspection, olfaction, inquiry, and palpation, analyzing and interpreting the data to make a diagnosis and offer appropriate treatment. Traditionally, the interpretation of this information relies heavily on the physician’s personal knowledge and experience. However, diagnostic outcomes can vary depending on the physician’s clinical experience and subjective judgment. This study employs AI methods to focus on localized tongue assessment, developing an automatic tongue body segmentation using the deep learning network “U-Net” through a series of optimization processes applied to tongue surface images. Furthermore, “ResNet34” is utilized for the identification of “cold”, “neutral”, and “hot” constitutions, creating a system that enhances the consistency and reliability of diagnostic results related to the tongue. The final results demonstrate that the AI interpretation accuracy of this system reaches the diagnostic level of junior TCM practitioners (those who have passed the TCM practitioner assessment with ≤5 years of experience). The framework and findings of this study can serve as (1) a foundational step for the future integration of pulse information and electronic medical records, (2) a tool for personalized preventive medicine, and (3) a training resource for TCM students learning to diagnose tongue constitutions such as “cold”, “neutral”, and “hot”. Full article
(This article belongs to the Special Issue Deep Learning for Computer Vision, 2nd Edition)
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