Data Augmentation Final Year Projects with Source Code

Byadmin Final Year Projects

Data Augmentation Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Data Augmentation 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.

Data Augmentation Final Year Projects

  1. A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images
    This project focuses on identifying how severe a brain tumor is using MRI images. It uses a light and fast deep learning model to classify tumors into low-grade or high-grade groups. The model is trained on public medical datasets and data from a local hospital. It shows very high accuracy compared to other popular deep learning models.
  2. 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.
  3. An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System
    This project focuses on improving early detection of breast cancer using computer-based tools. The researchers used advanced deep learning models, including EfficientNet, to analyze mammogram images. They combined multiple models together to make predictions more accurate and reliable. Their system achieved high accuracy in identifying both the type of abnormality and the disease itself.
  4. Data Augmentation Based on Generative Adversarial Networks for Endoscopic Image Classification
    This project aims to help doctors detect digestive system diseases more easily using computer-based image analysis. The system trains several deep learning models to automatically classify diseases from endoscopy images. It also creates extra training images using generative models to improve accuracy. The final model shows strong and safe performance, reducing the workload on medical staff.
  5. Explainable Artificial Intelligence EXAI Models for Early Prediction of Parkinsons Disease Based on Spiral and Wave Drawings
    This project aims to detect Parkinson’s disease early using advanced deep learning models. It combines two powerful neural networks to accurately distinguish patients from healthy individuals. The model is designed to be transparent, showing which parts of patient drawings influence its predictions. This approach helps doctors understand and trust the results, potentially improving early treatment and patient care.
  6. Looking Closer to the Transferability Between Natural and Medical Images in Deep Learning
    This study looks at how to improve medical image analysis using machine learning. It tests if knowledge from natural images can help in medical imaging. The researchers found that using natural images does not improve results much because medical images are very different. They also studied data enhancement techniques and found that these methods don’t transfer well from natural to medical images.
  7. Skin Medical Image Captioning Using Multi-Label Classification and Siamese Network
    This project develops a system that can automatically describe skin images using simple sentences. It uses multiple machine learning models to identify skin features, match keywords, and relate them to everyday language descriptions. The system achieved very high accuracy and can help teach dermatology, especially in hospitals or schools with limited resources. It makes learning skin diagnosis easier and supports practical training for medical students.
  8. Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
    This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
  9. Data Enrichment Toolchain A Data Linking and Enrichment Platform for Heterogeneous Data
    This project focuses on making large amounts of data from IoT devices, social media, and public portals more useful. It introduces a tool called the Data Enrichment Toolchain (DET) that organizes and connects data from different sources. DET adds meaning to raw data, links it together, and creates new information that can be analyzed more effectively. The project shows how this enriched data can improve public services and decision-making.
  10. A Deep Learning-Based Brain Age Prediction Model for Preterm Infants via Neonatal MRI
    This project develops a deep learning model called BAPNET to predict the brain age of premature infants using MRI scans. It helps estimate brain maturity quickly and accurately, reducing reliance on doctors’ manual assessments. The model learns from a large dataset of infant brain images and highlights important brain regions involved in development. This can support doctors in understanding brain growth and planning early interventions for preterm infants.
  11. An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition
    This project builds a system that can recognize handwritten Urdu characters using only a few sample images. It trains a model to learn patterns even when very little clean data is available. The system groups similar characters and uses these groups to identify new ones. It works better than many existing methods and gives higher accuracy with very limited training data.
  12. Generalization of Forgery Detection With Meta Deepfake Detection Model
    This project focuses on detecting fake videos and images created by face manipulation. It uses a deep learning approach that can learn from multiple sources and adapt to new, unseen types of fake media. The model trains in a way that improves its ability to generalize, so it can detect deepfakes without needing updates for each new type. Overall, it aims to make face forgery detection more reliable in real-world situations.
  13. Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network
    This project uses deep learning to identify patterns in electrical partial discharges from images. It combines two networks, CNN and LSTM, to improve detection accuracy. Data augmentation increases the number of training images. The proposed hybrid model achieves almost perfect accuracy in recognizing different PD types.
  14. SIGNFORMER DeepVision Transformer for Sign Language Recognition
    This project focuses on helping people communicate with the hearing impaired by recognizing sign language automatically. The researchers used a vision transformer model to identify static Indian sign language gestures. Their method breaks hand gestures into small parts and analyzes them using a transformer network. The system is accurate, fast to train, and performs better than previous approaches.
  15. Using Deep Learning Model to Identify Iron Chlorosis in Plants
    This project uses artificial intelligence to detect nutrient deficiencies in plant leaves. It analyzes leaf images and the soil to find the cause of the deficiency. Two deep learning models, SSD MobileNet v2 and EfficientDet D0, are tested. The models can classify leaves with high accuracy, and EfficientDet D0 gives the most precise results, though it takes more time to process.
  16. An AI Based Automatic Translator for Ancient Hieroglyphic LanguageFrom Scanned Images to English Text
    This project builds an AI system that can read ancient Egyptian hieroglyphs and translate them into English. It first identifies the symbols in scanned images and then converts them into clear English text. The goal is to help visitors understand historical sites more easily. The system uses different AI methods and shows strong results in both recognition and translation.
  17. Uncovering Archaeological Sites in Airborne LiDAR Data With Data-Centric Artificial Intelligence
    This project uses drones and AI to help archaeologists find burial mounds more efficiently. It turns 3D LiDAR data into 2D images and trains an AI model to identify likely sites. A special technique reduces mistakes by checking if the shapes match real mounds. This makes it easier for archaeologists to focus on promising locations in the field.
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