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Applied Sciences
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Recent Advances in and Applications of Medical Image Processing and...
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Recent Advances in and Applications of Medical Image Processing and Analysis

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 565

Special Issue Editors

Prof. Dr. Romuere Silva

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidade Federal do Piauí, Picos, Brazil
Interests: digital image processing; computer vision; artificial intelligence; bioinformatics
Dr. Antonio Oseas De Carvalho FIlho

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidade Federal do Piauí, Picos, Brazil
Interests: computer intelligence; computer vision; medical image processing; data analysis; computer graphics
Prof. Dr. Flávio Henrique Duarte de Araújo

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidade Federal do Piauí, Picos, Brazil
Interests: computer vision; deep learning; machine learning

Special Issue Information

Dear Colleagues,

In the ever-evolving field of healthcare, medical image processing and analysis have emerged as crucial pillars, revolutionizing diagnostics, treatment planning, and research. This Special Issue, titled "Recent Advances in and Applications of Medical Image Processing and Analysis," showcases the cutting-edge developments and practical applications in this dynamic domain. This collection of articles brings together experts, researchers, and innovators to present a comprehensive overview of the latest breakthroughs and their transformative impact on healthcare.

Prof. Dr. Romuere Silva
Dr. Antonio Oseas De Carvalho FIlho
Prof. Dr. Flávio Henrique Duarte de Araújo
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

  • medical imaging
  • image processing
  • machine learning
  • clinical applications
  • innovations

Published Papers (1 paper)

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Research

13 pages, 2233 KiB  
Article
Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
by Alan R. F. Santos, Kelson R. T. Aires and Rodrigo M. S. Veras
Appl. Sci. 2024, 14(8), 3297; https://doi.org/10.3390/app14083297 - 14 Apr 2024
Viewed by 375
Abstract
Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through [...] Read more.
Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis. Full article
(This article belongs to the Special Issue Recent Advances in and Applications of Medical Image Processing and Analysis)
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