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

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

This project focuses on improving network security by detecting intrusions quickly and accurately. It uses a type of artificial intelligence called convolutional neural networks with attention mechanisms. The method organizes network data into images in a smart way to make the detection process faster. Experiments show that this approach can identify threats efficiently while keeping high accuracy.
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images

This project focuses on detecting tuberculosis (TB) from chest X-ray images using a new deep learning model called CBAMWDnet. The model combines advanced techniques to better understand important features in the images. Tests on large datasets show it is very accurate and performs better than many existing models. This approach can help doctors diagnose TB earlier and more reliably.
Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images

This project focuses on using artificial intelligence to analyze chest X-rays for COVID-19 detection. The researchers developed a new method that can accurately identify infected areas, even when their size or location varies. The approach improves how the system understands both the position and details of the infection in the X-ray images. Tests on public datasets show it works better and more reliably than existing methods.
Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network

This project focuses on predicting a person’s brain age using MRI scans. It uses a special type of neural network to compare brain images from different people and measure their similarity. The model learns important features from the images and predicts brain age with good accuracy. Tests show it works well on a dataset of brain scans over time.
Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

This project focuses on detecting brain diseases using electrical impedance tomography, even when electrodes cannot be evenly placed. The researchers developed new ways to arrange electrodes and a smart method that considers these arrangements to locate brain bleeding accurately. Their approach was tested under many challenging conditions and showed very high accuracy and reliability. It performs better than traditional neural network methods for this task.
Malaria Disease Cell Classification With Highlighting Small Infected Regions

This project uses deep learning to detect malaria from images of red blood cells. The researchers created a method that focuses on the small infected regions in the cells, similar to how humans highlight important information. Their approach improved the accuracy of malaria detection on a public dataset to 97.2%, which is higher than standard models. The study shows that focusing on key areas in the images helps the neural network learn better.
Performance Improvement of Deep Learning Based Multi-Class ECG Classification Model Using Limited Medical Dataset

This study focuses on improving medical data classification when the dataset is unbalanced or limited. The researchers tested different ways to handle class imbalance, including changing loss functions, data amounts, and grouping methods. They used a deep learning model called Inception-V3 and found that using a special loss function called focal loss gave the best results. Their approach achieved very high accuracy, even when data were limited.
A Novel Spatio Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

This project focuses on improving driver assistance systems using only GPS data. The researchers developed a method to turn GPS trajectories into images and trained a neural network to analyze them. The system can accurately detect when a car is turning or going straight. It works better than existing methods and helps make driving safer and smarter.
Deep Learning-Based Optimization of VisualAuditory Sensory Substitution

This project focuses on helping visually impaired people understand their surroundings using sound instead of sight. The researchers used deep learning to improve a system that converts images into sounds. They tested how changing the system’s settings affects perception and compared the results with human experiments. The study shows that deep learning can make these systems more effective without relying heavily on human testing.
Identifying ADHD for Children With Coexisting ASD From fNIRs Signals Using Deep Learning Approach

This project focuses on detecting ADHD in children who also have autism. Researchers collected brain signals while children drew simple patterns. They used a deep learning method that combines CNN and Bi-LSTM to analyze these signals. The system could accurately identify children with ADHD and autism with over 90% accuracy.
Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing

This project focuses on making drone flights safer by detecting power lines. It uses a deep learning method called YOLO to find power lines in different shapes and positions. The algorithm improves detection by fixing missed lines and removing false ones. Tests showed it works better than older methods and can detect power lines in real time while the drone flies.
Toward Practical Deep Blind Watermarking for Traitor Tracing

This project focuses on protecting copyrighted images using a smart watermarking method. It combines deep learning with a special encoding strategy to hide watermarks without affecting image quality. The method splits images into small patches to make the watermark strong against distortions. Experiments show it keeps images clear, improves performance, and reduces training time and memory use.
STGL-GCN SpatialTemporal Mixing of Global and Local Self-Attention Graph Convolutional Networks for Human Action Recognition

This project focuses on recognizing human actions using skeleton data from videos. The method looks at both local and global connections between body joints to better understand movements. It uses a special neural network that learns which joint connections are most important for each action. Tests show it can accurately identify different human actions.
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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