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Related Experiment Video

Updated: Mar 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

MedScanGAN: Synthetic PET & CT Scan Generation Using Conditional Generative Adversarial Networks for Medical AI Data

Agorastos-Dimitrios Samaras¹, Ioannis D Apostolopoulos², Nikolaos Papandrianos¹

¹Department of Energy Systems, University of Thessaly, 413 34 Larisa, Greece.

Bioengineering (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

MedScanGAN generates realistic synthetic medical images for Non-Small-Cell Lung Cancer (NSCLC) diagnosis. Augmenting datasets with these images significantly improves AI diagnostic accuracy, particularly for lung nodule detection.

Keywords:

Non-Small-Cell Lung Cancer PET/CT computer-aided diagnosis data augmentation deep learning generative adversarial networks medical imaging synthetic data generation

Related Experiment Videos

Last Updated: Mar 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Data scarcity is a major challenge in developing AI for medical diagnosis.
Accurate diagnosis of Non-Small-Cell Lung Cancer (NSCLC) from medical images is critical.

Purpose of the Study:

To introduce MedScanGAN, a conditional Generative Adversarial Network for synthesizing high-fidelity PET and CT images of Solitary Pulmonary Nodules (SPNs).
To enhance computer-aided diagnosis (CADx) systems for NSCLC using generated synthetic data.

Main Methods:

Developed MedScanGAN, a conditional GAN with residual blocks, spectral normalization, and stabilized training.
Generated synthetic PET and CT images of SPNs.
Augmented training datasets of deep learning models (YOLOv8, VGG-16, ResNet, MobileNet) with synthetic data.

Main Results:

MedScanGAN generated realistic synthetic images, particularly for PET.
Augmenting datasets with synthetic data improved NSCLC classification performance.
YOLOv8 achieved 94.14% accuracy, 93.12% specificity, and 95.33% sensitivity with augmented data, a +5.8% accuracy gain.

Conclusions:

MedScanGAN demonstrates state-of-the-art medical image synthesis capabilities.
Synthetic data generated by MedScanGAN effectively improves the performance of deep learning models for NSCLC diagnosis.
This work bridges generative AI and clinical pulmonary oncology, offering practical value for diagnostic systems.