Transfer Learning Final Year Projects with Source Code

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

Transfer Learning Final Year Projects

  1. A Systematic Literature Review on Multimodal Machine Learning Applications Challenges Gaps and Future Directions
    This project reviews how machine learning can use multiple types of data together, like images, text, and audio, to solve real-world problems. It studies recent research on key challenges, such as combining, translating, and aligning these different data types. The authors analyzed over 1000 articles to identify trends, gaps, and progress in this area. This work helps researchers understand the current state of multimodal machine learning and plan future studies.
  2. A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework
    This project focuses on using artificial intelligence to detect and predict breast cancer while keeping patient data private. Instead of collecting all data in one place, it uses a federated learning system that learns from data in multiple locations without sharing sensitive information. The study improves accuracy by enhancing image data, balancing datasets, and using advanced AI models. Experiments show that this approach predicts cancer more accurately than traditional methods.
  3. 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.
  4. 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.
  5. Classification of Liver Fibrosis From Heterogeneous Ultrasound Image
    This project studies how artificial intelligence can help doctors diagnose liver problems using ultrasound images. The researchers found that AI models work well on images similar to those they were trained on but perform worse on images from different machines. They also explored ways to reduce errors caused by differences between machines and improved classification by combining similar categories. The work highlights the need for AI that performs reliably across different ultrasound devices.
  6. DeepSkin: A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using computer-based methods. It uses images of different skin lesions from a large dataset to teach a computer to recognize cancer types. Advanced deep learning models, like CNNs with DenseNet169 and ResNet50, are applied to improve accuracy. The goal is to help doctors identify skin cancer more reliably and quickly.
  7. 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.
  8. FieldPlant A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning
    This project focuses on improving the detection of plant diseases using images taken directly from farms. Researchers created a new dataset called FieldPlant, with over 5,000 real-field images carefully labeled by plant experts. They tested modern deep learning models on this dataset and found that these models performed better than when trained on previous datasets. The goal is to help farmers detect diseases more accurately and reduce food waste.
  9. GARL-Net Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification
    This project focuses on improving breast cancer detection using computer-based image analysis. The researchers developed a new deep learning method called GARL-Net that can learn more efficiently from large and uneven image datasets. It uses advanced techniques to reduce errors in classification and improve accuracy. Tests on popular breast cancer image datasets showed very high accuracy, precision, and recall, outperforming existing methods.
  10. Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model
    This project focuses on improving brain tumor detection using medical images. It combines two neural network models, CapsNet and VGGNet, to create a hybrid system that can automatically identify and classify tumors. The model works well even with smaller datasets and was tested on high-quality brain tumor images. It achieved very high accuracy, correctly identifying almost all tumors.
  11. 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.
  12. Interpretation of Magnetic Resonance Images of Temporomandibular Joint Disorders by Using Deep Learning
    This study uses deep learning to automatically detect Temporomandibular Disorder (TMD) from MRI scans. Researchers collected over 2500 images from 200 patients and tested several advanced neural network models to classify them. The performance of these models was measured using accuracy and other medical metrics. The results show that deep learning can successfully assist in diagnosing TMD.
  13. 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.
  14. Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval
    This project focuses on securely storing and retrieving medical images like X-rays, MRIs, and CT scans. It uses advanced deep learning techniques to extract important features from the images. The system encrypts images to keep them safe while allowing accurate searching and matching. Tests show that this method works better than existing approaches.
  15. 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.
  16. A hybrid method for identifying the feeding behavior of tilapia
    This project focuses on monitoring how tilapia fish eat in real time. The researchers improved a computer vision model called ResNet34 to better recognize fish feeding behavior. They added a module to help the model focus on important image features and used transfer learning to speed up training. The final model achieved very high accuracy, helping farmers decide the right amount of feed scientifically.
  17. Automated Red Palm Weevil Detection Using Gorilla Troops Optimizer With Deep Learning Model
    This project develops an automated system to detect Red Palm Weevil, a harmful pest for palm trees. It uses artificial intelligence and computer vision to analyze images and identify infected trees accurately. The system combines deep learning with an optimization algorithm to improve detection speed and accuracy. Tests show it can detect the pest with over 99% accuracy, helping protect plantations efficiently.
  18. 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.
  19. 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.
  20. DeepSkin A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using artificial intelligence. It uses a large dataset of skin images to teach a computer to recognize different types of skin lesions. The system processes images to remove noise and improve quality before training deep learning models. Advanced neural networks like DenseNet169 and ResNet50 are used to accurately classify the skin lesions.
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