Deep Neural Network Final Year Projects with Source Code

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Deep Neural Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Deep Neural Network 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.

Deep Neural Network Final Year Projects

  1. A Distracted Driving Detection Model Based On Driving Performance
    This project studies how a driver behaves when fully focused and when mentally distracted. Researchers collected driving data from many people using a simulator. They trained a deep learning model to recognize whether a driver is distracted just from the way they drive. The model works very well and can help detect unsafe driving in real time.
  2. A Lightweight Network Deployed on ARM Devices for Hand Gesture Recognition
    This project develops a lightweight system to recognize hand gestures. The system, called LHGR-Net, works on small devices like a Raspberry Pi. It can detect gestures in real time and use them to control home appliances. The method uses less memory but still performs almost as well as advanced models.
  3. A Novel Two-Stage Deep Learning Model for Network Intrusion Detection LSTM-AE
    This project focuses on improving computer systems’ ability to detect cyber-attacks automatically. It uses a combination of two advanced deep learning methods, LSTM and Auto-Encoders, to create a flexible and accurate intrusion detection system. The model is tested on publicly available datasets to find the best settings and compare its performance with other deep learning approaches. Results show that the proposed system can effectively detect attacks in modern network environments.
  4. A Review on Alzheimers Disease Through Analysis of MRI Images Using Deep Learning Techniques
    This project focuses on using brain MRI scans to detect Alzheimer’s disease early. It applies deep learning, especially convolutional neural networks, to analyze brain structures and identify signs of the disease. By examining the detailed tissue patterns, the method aims to improve accuracy in diagnosing Alzheimer’s. The study also reviews recent research and techniques showing how MRI segmentation helps in early detection.
  5. 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.
  6. 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.
  7. 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.
  8. Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection
    This project focuses on detecting insider threats, which are harmful actions by people who have authorized access to an organization’s network. Traditional security tools often fail, so the study uses machine learning to improve detection. It combines supervised and semi-supervised learning, analyzes data streams, and retrains models periodically. The best results were achieved using the Isolation Forest algorithm, showing good accuracy in identifying both harmful and safe activities.
  9. Classification of Polyps in Endoscopic Images Using Self-Supervised Structured Learning
    This project develops a smart computer system to identify types of polyps in medical images. It uses a neural network that can teach itself to focus on the whole polyp, even with few labeled images. The system improves accuracy by learning from both medical and natural images before fine-tuning on polyps. The result is a more reliable classification of polyps as either hyperplastic or tubular adenoma.
  10. Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning
    This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.
  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. Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering
    This project focuses on helping doctors quickly understand medical images by identifying the type of imaging technique used, like X-rays or skin scans. The researchers combine visual patterns and texture details from images using deep learning to extract important features. They then merge these features to improve accuracy in classifying the images. Their method shows high precision and recall, making medical image analysis faster and more reliable.
  13. Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
    This project focuses on improving brain tumor detection from MRI images using artificial intelligence. The researchers developed a deep learning model that automatically processes and classifies images, reducing errors and saving time. They used special filters and mathematical techniques to extract important features from the images. The model achieved very high accuracy of 98.8% when tested on brain tumor data.
  14. Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases
    This project uses deep learning to predict heart diseases early from patient medical records. The researchers trained a model on over 2,600 records, including age, symptoms, and disease details. They used a special neural network called LSTM to improve prediction by analyzing combinations of symptoms. Their method achieved up to 91% accuracy in predicting cardiovascular problems.
  15. 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.
  16. 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.
  17. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  18. Distributed Split Computing System in Cooperative Internet of Things IoT
    This project focuses on improving how IoT devices work together to process data. Instead of sending all tasks to the cloud, nearby IoT devices share the computing work. The system decides which devices should help based on energy use and speed. This approach reduces energy consumption by over 20% while ensuring tasks finish on time.
  19. Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network
    This project uses Internet of Things (IoT) devices in farms to monitor environmental conditions like temperature, humidity, rainfall, wind, and sunshine. A deep learning model uses this data to predict pest outbreaks in crops. The system generates weekly predictions with high accuracy, helping farmers take timely action to protect crops. Over five years of data, the model achieved 94% accuracy and improves predictions over time.
  20. Neural-Hill A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability
    This project focuses on making smart devices work more efficiently with cloud servers. It introduces a new method called Neural-Hill, which combines AI and optimization techniques to manage cloud resources for Internet of Things (IoT) devices. The system helps process tasks faster, reduces delays, and handles more devices without slowing down. Experiments show it improves service quality and scales well as more devices connect.
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