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Technologies
Special Issues
Breakthroughs in Bioinformatics and Biomedical Engineering
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Breakthroughs in Bioinformatics and Biomedical Engineering

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 484

Special Issue Editors

Prof. Dr. Panayiotis Tsanakas

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15772 Athens, Greece
Interests: biomedical informatics; high-performance systems; distributed systems and algorithms; cloud/edge computing
Dr. Evangelos Makris

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, 15772 Athens, Greece
Interests: bioinformatics; pattern recognition; artificial intelligence; activity recognition

Special Issue Information

Dear Colleagues,

This Special Issue explores cutting-edge advancements in bioinformatics and biomedical engineering, focusing on leveraging technology to solve challenges in medicine and biology. We invite high-quality original research, reviews, and case studies demonstrating novel applications of artificial intelligence (AI), machine learning (ML), and computational methods in healthcare, bioinformatics, and medical imaging. Topics will highlight innovations in bioinformatics, medical image analysis, and AI-driven diagnostics that enhance the understanding and treatment of diseases.

Prof. Dr. Panayiotis Tsanakas
Dr. Evangelos Makris
Guest Editor

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. Technologies is an international peer-reviewed open access monthly 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 1600 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

  • bioinformatics
  • medical image analysis
  • artificial intelligence in medicine
  • deep learning
  • machine learning in healthcare
  • computational genomics
  • RNA sequencing
  • predictive modeling
  • wearable health devices

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

37 pages, 3382 KiB  
Article
Multi-Domain Feature Incorporation of Lightweight Convolutional Neural Networks and Handcrafted Features for Lung and Colon Cancer Diagnosis
by Omneya Attallah
Technologies 2025, 13(5), 173; https://doi.org/10.3390/technologies13050173 - 25 Apr 2025
Viewed by 95
Abstract
This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. The system uses several light deep networks (EfficientNetB0, MobileNet, and ResNet-18), which feature fewer layers and parameters, unlike traditional systems that depend on a single, parameter-complex deep [...] Read more.
This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. The system uses several light deep networks (EfficientNetB0, MobileNet, and ResNet-18), which feature fewer layers and parameters, unlike traditional systems that depend on a single, parameter-complex deep network. Additionally, it employs several handcrafted feature extraction techniques. It systematically assesses the diagnostic power of deep features only, handcrafted features alone, and both deep and handcrafted features combined. Furthermore, it examines the influence of combining deep features from multiple CNNs with distinct handcrafted features on diagnostic accuracy, providing insights into the effectiveness of this hybrid approach for classifying lung and colon cancer. To achieve this, the proposed CAD employs non-negative matrix factorization for lowering the dimension of the spatial deep feature sets. In addition, these deep features obtained from each network are distinctly integrated with handcrafted features sourced from temporal statistical attributes and texture-based techniques, including gray-level co-occurrence matrix and local binary patterns. Moreover, the CAD integrates the deep attributes of the three deep networks with the handcrafted attributes. It also applies feature selection based on minimum redundancy maximum relevance to the integrated deep and handcrafted features, guaranteeing optimal computational efficiency and high diagnostic accuracy. The results indicated that the suggested CAD system attained remarkable accuracy, reaching 99.7% using multi-modal features. The suggested methodology, when compared to present CAD systems, either surpassed or was closely aligned with state-of-the-art methods. These findings highlight the efficacy of incorporating multi-domain attributes of numerous lightweight deep learning architectures and multiple handcrafted features. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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14 pages, 12491 KiB  
Article
Biomechanical Evaluation of Elliptical Leaf Spring Prosthetics for Unilateral Transtibial Amputees During Dynamic Activities
by Qiu-Qiong Shi, Kit-Lun Yick, Chu-Hao Li, Chi-Yung Tse and Chi-Hang Hui
Technologies 2025, 13(4), 129; https://doi.org/10.3390/technologies13040129 - 30 Mar 2025
Viewed by 281
Abstract
This study explores the biomechanical impact of an elliptical leaf spring (ELS) foot on individuals with unilateral below-knee amputation. The ELS-foot, constructed with carbon fiber leaf springs and an ethylene-vinyl acetate rocker bottom sole, aims to balance energy storge and dissipation for effective [...] Read more.
This study explores the biomechanical impact of an elliptical leaf spring (ELS) foot on individuals with unilateral below-knee amputation. The ELS-foot, constructed with carbon fiber leaf springs and an ethylene-vinyl acetate rocker bottom sole, aims to balance energy storge and dissipation for effective cushioning and energy management. Six participants were recruited and visited the laboratory twice within a 3-to-5-day interval. The ELS-foot is compared with their own prosthesis through various mobility and balance tests, including the Timed Up and Go test, Four Square Step Test, 10 m walk test, Berg Balance Test, eyes-closed standing test, Tandem Test, jumping and walking test, and a subjective evaluation. Passive-reflective markers are placed on the participants according to the plug-in full body model. An eight-camera motion capture system synced with two force plates mounted under a walkway is used for the gait analysis. The results show that participants move faster during the Four Square Step Test and demonstrate better balance during the eyes-closed standing test and Tandem Test and jump higher with the ELS-foot. The unique ELS-foot design mechanism and rocker bottom sole facilitates better energy transfer and stability, thus enhancing the postural stability. These findings offer valuable insights for future prosthetic technology advancements. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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