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Deep Learning and Multimodal Feature Fusion for Advanced Medical Imaging...
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Deep Learning and Multimodal Feature Fusion for Advanced Medical Imaging Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 591

Special Issue Editors

Dr. Sarmad Maqsood

E-Mail Website
Guest Editor
1. Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology, LT-51386 Kaunas, Lithuania
2. Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
Interests: deep learning; feature fusion; medical diagnosis; image processing
Prof. Dr. Rytis Maskeliūnas

E-Mail Website
Co-Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland
Interests: deep learning; feature fusion; multimodal; classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of deep learning in multimodal feature fusion is emerging as a transformative approach in medical imaging, offering promising opportunities for enhanced diagnostic accuracy, efficiency, and clinical insight. This Special Issue seeks to gather state-of-the-art research that applies artificial intelligence to advance diagnostic imaging across a wide range of clinical applications.

The focus is on innovative methods that combine imaging data such as MRI, CT, PET, and ultrasound with complementary clinical information, including electronic health records, laboratory findings, and patient history. The goal is to promote techniques that leverage the strengths of data-driven models to support early disease detection, precise characterization, and effective decision making.

Topics of interest include, but are not limited to, the following:

  • Deep learning-based classification, segmentation, detection, and reconstruction;
  • Multimodal feature fusion integrating image-based and non-image-based data;
  • Explainable and trustworthy AI for clinical interpretation;
  • Transfer learning, few-shot learning, and domain adaptation in medical imaging;
  • Dataset benchmarking, reproducibility, and clinical validation studies.

We welcome original research articles, review papers, and case studies that contribute to advancing the field of AI-powered medical diagnostics through deep learning and feature fusion.

Dr. Sarmad Maqsood
Prof. Dr. Rytis Maskeliūnas
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. Diagnostics 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 2600 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
  • feature fusion
  • medical imaging
  • artificial intelligence in healthcare
  • diagnostic imaging
  • multimodal data integration
  • explainable AI
  • clinical decision support

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Published Papers (1 paper)

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Research

23 pages, 5584 KiB  
Article
Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer
by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang and Bo Chen
Diagnostics 2025, 15(14), 1730; https://doi.org/10.3390/diagnostics15141730 - 8 Jul 2025
Viewed by 351
Abstract
Background: The rapid advancement of radiomics and artificial intelligence (AI) technology has provided novel tools for the diagnosis of esophageal cancer. This study innovatively combines muscle imaging features with conventional esophageal imaging features to construct deep learning diagnostic models. Methods: This [...] Read more.
Background: The rapid advancement of radiomics and artificial intelligence (AI) technology has provided novel tools for the diagnosis of esophageal cancer. This study innovatively combines muscle imaging features with conventional esophageal imaging features to construct deep learning diagnostic models. Methods: This retrospective study included 1066 patients undergoing radical esophagectomy. Preoperative computed tomography (CT) images covering esophageal, stomach, and muscle (bilateral iliopsoas and erector spinae) regions were segmented automatically with manual adjustments. Diagnostic models were developed using deep learning (2D and 3D neural networks) and traditional machine learning (11 algorithms with PyRadiomics-derived features). Multimodal features underwent Principal Component Analysis (PCA) for dimension reduction and were fused for final analysis. Results: Comparative analysis of 1066 patients’ CT imaging revealed the muscle-based model outperformed the esophageal plus stomach model in predicting N2 staging (0.63 ± 0.11 vs. 0.52 ± 0.11, p = 0.03). Subsequently, multimodal fusion models were established for predicting pathological subtypes, T staging, and N staging. The logistic regression (LR) fusion model showed optimal performance in predicting pathological subtypes, achieving accuracy (ACC) of 0.919 in the training set and 0.884 in the validation set. For predicting T staging, the support vector machine (SVM) model demonstrated the highest accuracy, with training and validation accuracies of 0.909 and 0.907, respectively. The multilayer perceptron (MLP) fusion model achieved the best performance among all models tested for N staging prediction, although the accuracy remained moderate (ACC = 0.704 in the training set and 0.685 in the validation set), indicating potential for further optimization. Fusion models significantly outperformed single-modality models. Conclusions: Based on CT imaging data from 1066 patients, this study systematically constructed predictive models for pathological subtypes, T staging, and N staging of esophageal cancer. Comparative analysis of models using esophageal, esophageal plus stomach, and muscle modalities demonstrated that muscle imaging features contribute to diagnostic accuracy. Multimodal fusion models consistently showed superior performance. Full article
(This article belongs to the Special Issue Deep Learning and Multimodal Feature Fusion for Advanced Medical Imaging Diagnosis)
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