Data Augmentation Techniques Final Year Projects with Source Code

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

  1. A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images
    This project uses eye scan images to detect and outline fluid-filled cysts inside the retina. It trains a deep learning model to automatically find these cysts, which normally takes doctors a lot of time to do by hand. The system can identify different types of cysts and helps doctors understand eye diseases better. It also performs better than many existing methods.
  2. An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
    This project develops an AI-based system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to analyze images and identify diseases more accurately than traditional methods. The system improves image data with techniques like rotation and flipping and combines multiple models to make reliable predictions. Experiments show it achieves around 97% accuracy, helping doctors make faster and better treatment decisions.
  3. EfficientNetB3-Adaptive Augmented Deep Learning AADL for Multi-Class Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It uses advanced deep learning models that have already been trained on large datasets to recognize 52 types of diseases and healthy leaves. The study tested several models and found that one called EfficientNetB3-AADL gave the most accurate results, correctly identifying diseases 98.7% of the time. This approach can help farmers quickly and accurately detect plant diseases to protect crops.
  4. Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network
    This project focuses on detecting early-stage Alzheimer’s disease using patterns in people’s handwriting captured online. Since there is limited data available, the study uses a special type of artificial intelligence, called DoppelGANger, to generate realistic handwriting examples. These generated examples help train a neural network to recognize Alzheimer’s more accurately. The approach was tested on real handwriting data and showed much better results than existing methods.
  5. Deep vs. Shallow A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
    This project focuses on detecting fake health news on the internet. It compares two types of models: one that uses only the news text and another that also considers readability features. Different machine learning and deep learning methods were tested. The study found that using readability features improves detection, and the AdaBoost-Random Forest model gave the best results.
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