Skip to main content

Call for papers - Image classification

Guest Editor

Mohammed Abdelsamea, PhD, University of Exeter, UK

Submission Status: Open   |   Submission Deadline: 16 January 2026

BMC Medical Imaging is calling for submissions to our Collection on Image classification. This Collection invites research focused on image classification in medical imaging, exploring innovative techniques, real-world applications, and challenges in automated image analysis, to highlight the potential of image classification to improve medical decision-making processes and patient outcomes.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

Meet the Guest Editor

Back to top

Mohammed Abdelsamea, PhD, University of Exeter, UK

Dr Mohammed Abdelsamea  is a senior lecturer in computer science, specializing in machine learning and computer vision at the University of Exeter, and a fellow of the British Higher Education Academy. His research is focused on developing explainable AI solutions to assist human investigation in healthcare and data science. Dr Abdelsamea's work includes explainable AI for healthcare, energy functional optimization for computer vision, machine teaching, active learning, uncertainty quantification, self-supervision, transfer learning, deep ensemble methods, multimodal learning systems, and causality AI. He holds a PhD from Scuola IMT Alti Studi Lucca, Italy, and serves as an Associate Editor for Soft Computing, BMC Medical Informatics and Decision Making and PLOS ONE.

About the Collection

BMC Medical Imaging is calling for submissions to our Collection on Image classification. The field of image classification offers ways to analyze and interpret medical images to detect various medical conditions from imaging modalities including X-ray, MRI, and CT scans. Image classification can be automated through the use of machine learning techniques, particularly convolutional neural networks. This Collection seeks to explore the latest methodologies and applications in image classification, showcasing the potential of these technologies to enhance diagnostic accuracy and clinical decision-making.

The relevance of automated image analysis in medical settings has gained considerable attention, especially with the increasing volume of imaging data generated in clinical practice. Recent advancements have demonstrated that machine learning models can outperform traditional diagnostic methods in specific scenarios, leading to faster and more reliable diagnoses. The integration of image classification technologies is becoming increasingly important in supporting healthcare professionals in their decision-making processes.

Future advancements in image classification may lead to even more sophisticated and generalizable algorithms. The application of explainable AI in medical imaging could also enhance trust and acceptance among clinicians by providing insights into the decision-making processes of automated systems. As technology progresses, we may witness the emergence of fully integrated diagnostic systems that utilize real-time imaging analysis.

This Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Image credit: © [M] NicoElNino / Getty Images / iStock

  1. Breast cancer remains the most commonly diagnosed malignancy among women worldwide. Histopathological image analysis is the clinical gold standard for diagnosis; however, the high resolution and complexity of ...

    Authors: Yuee Zhou, Fengqing Jin, Guodong Suo and Jianlan Yang
    Citation: BMC Medical Imaging 2025 25:401
    Content type: Research Published on:

Submission Guidelines

Back to top

This Collection welcomes submission of original Research, Software, Study protocol, and Systematic Review Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Image classification" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.