Special Issues
Table of Content

Advanced Medical Imaging Techniques Using Generative Artificial Intelligence

Submission Deadline: 01 May 2025 (closed) View: 935

Guest Editors

Prof. Nicu Bizon, The National University of Science and Technology POLITEHNICA Bucharest, Romania
Prof. Bhargav Appasani, Kalinga Institute of Industrial Technology, India

Summary

This issue will focus on the theme of Advanced Medical Imaging Techniques using Generative Artificial Intelligence, which are reshaping medical image processing and its interpretation.


The scope encompasses various aspects, including image reconstruction, synthesis, anomaly detection, segmentation, registration, fusion, and clinical decision support systems, all empowered by generative AI techniques. By discerning intricate patterns and correlations within medical images, these advanced AI models empower early disease prediction and accurate diagnosis. Ethical considerations and challenges inherent in the integration of these techniques in medical imaging need to be studied. Authors are encouraged to submit original research articles, reviews, and perspectives contributing to the understanding and advancement of this theme. Topics of interest include, but are not limited to:

  

Topics of interest of this Special Issue include, but are not limited to:

Generative Adversarial Networks (GANs) for medical image reconstruction

Realistic medical image generation using GANs

Anomaly Detection in CT Scans using generative AI

Medical image resolution enhancement using generative AI

Multi-modal image fusion using generative AI

Automated segmentation using GANs

Autoencoders for medical image analysis

Data augmentation using generative AI

Automated Tumor Detection using GANs

Privacy-preserving image synthesis using generative AI

Transfer Learning with generative models

3D reconstruction using generative AI

Ethical considerations in using generative AI for medical image analysis


Keywords

Generative AI
Generative Adversarial Networks
Autoencoders
Artificial intelligence
Medical imaging
Diagnosis
Tumor detection
Segmentation techniques
Synthetic data augmentation
Model bias
Ethical considerations
This special issue is also a special session on the Electronics, Computers and Artificial Intelligence (ECAI'24) CONFERENCE (https://ecai.ro/ ; deadline: 2 June, 2024), where you can submit 4-6 pages and then the extended version here.

Published Papers


  • Open Access

    ARTICLE

    An Advanced Medical Diagnosis of Breast Cancer Histopathology Using Convolutional Neural Networks

    Ahmed Ben Atitallah, Jannet Kamoun, Meshari D. Alanazi, Turki M. Alanazi, Mohammed Albekairi, Khaled Kaaniche
    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5761-5779, 2025, DOI:10.32604/cmc.2025.063634
    (This article belongs to the Special Issue: Advanced Medical Imaging Techniques Using Generative Artificial Intelligence)
    Abstract Breast Cancer (BC) remains a leading malignancy among women, resulting in high mortality rates. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic tools, while effective, have limitations that reduce their accessibility and accuracy. This study investigates the use of Convolutional Neural Networks (CNNs) to enhance the diagnostic process of BC histopathology. Utilizing the BreakHis dataset, which contains thousands of histopathological images, we developed a CNN model designed to improve the speed and accuracy of image analysis. Our CNN architecture was designed with multiple convolutional layers, max-pooling layers, and a fully connected… More >

  • Open Access

    ARTICLE

    Harmonization of Heart Disease Dataset for Accurate Diagnosis: A Machine Learning Approach Enhanced by Feature Engineering

    Ruhul Amin, Md. Jamil Khan, Tonway Deb Nath, Md. Shamim Reza, Jungpil Shin
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645
    (This article belongs to the Special Issue: Advanced Medical Imaging Techniques Using Generative Artificial Intelligence)
    Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

Share Link