Convolutional Neural Network Architecture Final Year Projects with Source Code
Convolutional Neural Network Architecture Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Convolutional Neural Network Architecture 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.
Convolutional Neural Network Architecture Final Year Projects
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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. -
A Nested Attention Guided UNet Architecture for White Matter Hyperintensity Segmentation
This project focuses on improving the detection of White Matter Hyperintensity (WMH) in brain MRI scans, which is important for predicting recovery in stroke patients. The researchers developed a new deep learning method called NAUNet++ that uses attention mechanisms and atlas images to better identify WMH regions. Their approach produces more accurate and faster segmentation results than existing methods, helping doctors assess patient prognosis more reliably. -
Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images
This project focuses on detecting early signs of diabetic eye disease from retinal images. It creates a method to generate and highlight disease spots using a small set of open-source images. A custom neural network is developed to classify these spots accurately. The system performs very well, achieving perfect results on the test data. -
The Key Criteria for Predicting Unusual Behavior in the Elderly With Deep Learning Models Under 5G Technology
This study explores using advanced AI methods to detect unusual behavior in elderly people at home. It uses 5G networks to collect and analyze data quickly and efficiently. The researchers first identified the most important factors affecting elderly behavior using a decision-making method called DEMATEL. Then, they applied deep learning models, CNN and LSTM, to detect abnormal behavior. The results showed that LSTM worked best, achieving 96% accuracy, and highlighted depression as a key factor influencing unusual behavior. -
5G Aviation Networks Using Novel AI Approach for DDoS Detection
This project develops an intelligent system to detect cyberattacks at airports using 5G networks. It converts network data into images and uses a combination of convolutional and recurrent neural networks to identify threats. The system achieves high accuracy in detecting attacks and performs well on multiple benchmark datasets. This approach helps improve security in modern smart airport infrastructures. -
SENext Squeeze-and-ExcitationNext for Single Image Super-Resolution
This project focuses on improving low-resolution images to high-resolution images using a deep learning method. The researchers designed a new network called SENext, which reduces computation and memory needs while keeping high image quality. It uses special blocks to enhance important features and skip connections to reuse information efficiently. Tests show that SENext is faster, uses fewer resources, and produces sharper and clearer images compared to existing methods.
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Convolutional Neural Network Architecture Project Synopsis & Presentation
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