Vision Transformer Final Year Projects with Source Code

Byadmin Final Year Projects

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

Vision Transformer Final Year Projects

  1. A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network
    This project focuses on improving medical image segmentation, which helps computers identify regions like tumors in medical scans. Traditional methods using neural networks struggle to capture both small details and overall structures. The researchers combined ideas from Transformers and visual attention networks to create a new model called M-VAN Unet. This model uses special attention methods to better learn detailed and global features, and experiments show it performs better than existing methods.
  2. Application of X-Ray Imaging and Convolutional Neural Networks in the Prediction of Tomato Seed Viability
    This project focuses on predicting whether tomato seeds will grow successfully without damaging them. The researchers used X-ray images of seeds to check their internal structure. They created two prediction models: one based on image analysis and another using a type of artificial intelligence called a convolutional neural network (CNN). The CNN model was more accurate, achieving 86% accuracy, showing that this method can help farmers and scientists test seed quality quickly and safely.
  3. Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network
    This project focuses on classifying ancient bone sticks unearthed in China using artificial intelligence. It develops a deep learning model that can accurately identify fracture locations and colors on the bone sticks. The model uses advanced attention techniques to focus on important features and reduce background interference. As a result, it achieves high accuracy, making the classification of these historical artifacts much faster and more reliable.
  4. 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.
  5. Medical Image Segmentation Based on Transformer and HarDNet Structures
    This project improves medical image segmentation, which helps doctors detect diseases more accurately. It uses a new network with two encoders to capture both local details and overall image features. A special fusion module combines information from different layers to boost accuracy. Tests on several medical datasets show better results in identifying disease areas, helping in early diagnosis and treatment.
  6. Medical Ultrasound Image Segmentation With Deep Learning Models
    This project focuses on improving the analysis of medical ultrasound images. The researchers created a new model called ConvTrans-Net that combines a transformer and a deep neural network. It helps accurately identify and segment lesion areas in ultrasound scans. The model showed high precision and recall, making it more effective than some existing methods.
  7. Multi-Camera 3D Object Detection for Autonomous Driving Using Deep Learning and Self-Attention Mechanism
    This project focuses on detecting 3D shapes of vehicles using only regular cameras, without needing special depth sensors. It uses multiple cameras installed on the road to gather information and predicts the vehicle's position and orientation. A vision transformer improves accuracy, especially in difficult or blocked views. Finally, it combines data from all cameras to choose the most reliable 3D detection.
  8. 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.
  9. TnTViT-G Transformer in Transformer Network for Guidance Super Resolution
    This project focuses on improving the quality of low-resolution images from sensors, especially expensive ones like infrared cameras. It uses a cheaper visible-light image to guide the enhancement of the infrared image. The method employs a dual-stream Transformer network called TnTViT-G to extract and combine features from both images. The model can create high-quality images of any size and performs better than existing approaches while using less memory.
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Vision Transformer Project Synopsis & Presentation

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