Conditional Generative Adversarial Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Conditional Generative Adversarial Network projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Conditional Generative Adversarial Network Final Year Projects

Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation

This project develops a smart system to detect diseases in chest X-rays. It works by creating a “normal” version of a given X-ray and then highlighting differences between the original and normal images. These differences point to possible diseased areas. The system uses these highlighted areas to improve detection accuracy and is tested on large public X-ray datasets.
Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs into 3 Dimensional Views

This project develops a method to convert 2-D medical images like X-rays into 3-D views. It uses a specialized deep learning model that can show the organ from all angles. The system cleans and standardizes the images before processing, and it is designed to work even with noisy or unclear inputs. Tests on real hospital data show that the generated 3-D images preserve important details and match the quality of the original scans.
Facial Expression Transfer Based on Conditional Generative Adversarial Networks

This project focuses on transferring facial expressions from one face to another using advanced computer vision. It uses a special neural network model that combines key facial features from a source and target face. The model creates realistic images that keep the target person's identity while showing the new expression. Experiments show it works better and faster than previous methods.
MACGAN An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN

This project improves the clarity of images for ground, aerial, and underwater navigation in difficult conditions like fog, rain, snow, and murky water. It uses a single lightweight network called MACGAN that focuses on the most important features of an image to restore visibility. The network works across many environments using the same settings, making it efficient and robust. Tests show it performs better than existing methods and handles real-world conditions well.

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Conditional Generative Adversarial Network Final Year Projects with Source Code