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Diagnostics
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Deep Learning and Multimodal Large Language Models in Tumor/Cancer Diagnosis,...
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Deep Learning and Multimodal Large Language Models in Tumor/Cancer Diagnosis, Prediction and Prognosis

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 September 2025 | Viewed by 696

Special Issue Editor

Dr. Steven L. Fernandes

E-Mail Website
Guest Editor
Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, USA
Interests: CT; machine learning; deep learning; tumor classification; medical images segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Deep Learning and Multimodal Large Language Models in Tumor/Cancer Diagnosis, Prediction and Prognosis”, aims to delve into the transformative role of advanced computational techniques in revolutionizing oncology. It explores how deep learning algorithms and multimodal large language models are enhancing the precision of tumor detection, facilitating early diagnosis, and improving prediction for cancer progression. By showcasing cutting-edge research and real-world applications, this collection aims to illuminate the path forward in personalized medicine, ultimately contributing to better patient outcomes and advancing our understanding of complex oncological processes.

Dr. Steven L. Fernandes
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. 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

  • diagnosis
  • prognosis
  • tumor/cancer
  • deep learning
  • multimodal large language models

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Published Papers (1 paper)

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Research

18 pages, 1914 KB  
Article
Hybrid of VGG-16 and FTVT-b16 Models to Enhance Brain Tumors Classification Using MRI Images
by Eman M. Younis, Ibrahim A. Ibrahim, Mahmoud N. Mahmoud and Abdullah M. Albarrak
Diagnostics 2025, 15(16), 2014; https://doi.org/10.3390/diagnostics15162014 - 12 Aug 2025
Viewed by 454
Abstract
Background: The accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is pivotal for timely clinical intervention, yet remains challenged by tumor heterogeneity, morphological variability, and imaging artifacts. Methods: This paper presents a novel hybrid approach for improved brain [...] Read more.
Background: The accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is pivotal for timely clinical intervention, yet remains challenged by tumor heterogeneity, morphological variability, and imaging artifacts. Methods: This paper presents a novel hybrid approach for improved brain tumor classification and proposes a novel hybrid deep learning framework that amalgamates the hierarchical feature extraction capabilities of VGG-16, a convolutional neural network (CNN), with the global contextual modeling of FTVT-b16, a fine-tuned vision transformer (ViT), to advance the precision of brain tumor classification. To evaluate the recommended method’s efficacy, two widely known MRI datasets were utilized in the experiments. The first dataset consisted of 7.023 MRI scans categorized into four classes gliomas, meningiomas, pituitary tumors, and no tumor. The second dataset was obtained from Kaggle, which consisted of 3000 scans categorized into two classes, consisting of healthy brains and brain tumors. Results: The proposed framework addresses critical limitations of conventional CNNs (local receptive fields) and pure ViTs (data inefficiency), offering a robust, interpretable solution aligned with clinical workflows. These findings underscore the transformative potential of hybrid architectures in neuro-oncology, paving the way for AI-assisted precision diagnostics. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.46% and 99.90%, respectively. Conclusions: Future work will focus on multi-institutional validation and computational optimization to ensure scalability in diverse clinical settings. Full article
(This article belongs to the Special Issue Deep Learning and Multimodal Large Language Models in Tumor/Cancer Diagnosis, Prediction and Prognosis)
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