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Novel Insights into Medical Images Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 437

Special Issue Editors


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Guest Editor
Department of Computational Science, Instituto Nacional de Astrofisica, Optica y Electronica (INAOE), Puebla 72840, Mexico
Interests: natural language processing; machine learning; affective information; irony detection; sentiment analysis

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Guest Editor
Departamento de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, San Andres Cholula 72840, Mexico
Interests: image processing; medical imaging
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Special Issue Information

Dear Colleagues,

Advances in medical technology have generated new sources of information used to understand the behavior of several diseases through images. The analysis of medical images is important for processing information from different tasks, such as the use of CAD tools, diagnosis assistance, monitoring, individual treatments, remote health care, and case identification, among others. Interdisciplinary research is of utmost importance to continue to improve our knowledge and the development of new technologies. Moreover, access to new images has allowed us to observe information beyond our visual abilities.

However, this new information facilitates the analysis and identification of health conditions. We must use new optical, computational, and electronic methods to acquire and study these medical images.

This Special Issue aims to compile new contributions to medical image processing from acquisition to analysis and interpretation. Contributions from other areas and multidisciplinary research are welcome. We warmly encourage you to contribute to this collection in Applied Sciences.

Areas of focus include, but are not limited to, image processing and analysis; medical image devices; image classification and segmentation; machine learning and deep learning for medical images; optical and photonics biosensing; development of CADe and CADx tools; medical measurements and instrumentation; biomedical image analysis; virtual reality in medicine; IoT in medicine; biosignal processing; and embedded systems.

Dr. Irazú Hernández Farías
Dr. Hayde Peregrina-Barreto
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

  • image processing
  • image analysis
  • medical image devices
  • image classification
  • image segmentation

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Published Papers (1 paper)

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Research

17 pages, 2781 KiB  
Article
Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning
by Suphakon Jarujunawong and Paramate Horkaew
Appl. Sci. 2025, 15(12), 6883; https://doi.org/10.3390/app15126883 - 18 Jun 2025
Viewed by 190
Abstract
Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features [...] Read more.
Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features in invasive ductal carcinoma classification using whole slide imaging. Our results showed that orientation invariant filters achieved an accuracy of 0.8125, F1-score of 0.8134, and AUC of 0.8761, while preserving cellular arrangement and tissue morphology. By utilizing spatial relationships across varying extents, the proposed fusion strategy aligns with pathological interpretation principles. While integrating Gabor wavelet responses into ResNet-50 enhanced feature association, the comparative analysis emphasized the benefits of weighted morphological fusion, further strengthening diagnostic performance. These insights underscore the crucial role of informative filters in advancing DL schemes for breast cancer screening. Future research incorporating diverse, multi-center datasets could further validate the approach and broaden its diagnostic applications. Full article
(This article belongs to the Special Issue Novel Insights into Medical Images Processing)
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