Deep Learning Models Final Year Projects with Source Code

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

  1. A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture
    This project helps identify diseases in fish using computer analysis of underwater images. It improves the accuracy of detection even when images are unclear. The system focuses on the important parts of the fish and learns quickly from new images. It can support farmers in keeping fish healthy and improving production.
  2. A Comprehensive Joint Learning System to Detect Skin Cancer
    This project builds a smart system that helps doctors detect skin diseases early. It studies images of the skin and learns patterns that indicate different types of diseases. The system combines two learning methods to improve accuracy. It achieves very high accuracy in identifying multiple skin conditions.
  3. A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images
    This project uses eye scan images to detect and outline fluid-filled cysts inside the retina. It trains a deep learning model to automatically find these cysts, which normally takes doctors a lot of time to do by hand. The system can identify different types of cysts and helps doctors understand eye diseases better. It also performs better than many existing methods.
  4. 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.
  5. A Privacy-Preserving Learning Method for Analyzing HEV Drivers Driving Behaviors
    This project focuses on analyzing how electric and hybrid vehicle drivers behave while driving. Instead of using cameras or GPS that can reveal personal information, it collects data directly from the car’s onboard system. The system uses advanced deep learning models to learn driving patterns and predict risky behavior. When a risky behavior is detected, the car dashboard shows an alert, helping improve safety while keeping driver privacy protected.
  6. A Quantitative Logarithmic Transformation-Based Intrusion Detection System
    This project focuses on building a system to detect network attacks without using complex machine learning. It analyzes network behavior using simple statistical methods, making it fast and easy to run in real time. The system can identify different types of attacks in both real and simulated network traffic. Tests show it detects threats accurately, even when data is limited.
  7. 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.
  8. A Systematic Literature Review on Plant Disease Detection Motivations Classification Techniques Datasets Challenges and Future Trends
    This study reviews research on detecting plant pests and diseases using artificial intelligence. The authors analyzed 176 important papers from over 1300 studies to understand how AI, machine learning, and deep learning are used with images and datasets. They found that some methods, like SVMs and cognitive CNNs, perform well, but detecting the exact disease location remains a challenge. The study highlights the need for lightweight models that work on small devices and across many crops.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. Classification of Diabetic Retinopathy Disease Levels by Extracting Topological Features Using Graph Neural Networks
    This project focuses on improving the detection of diabetic retinopathy, a major cause of blindness, from retinal images. It uses a new deep learning approach that combines feature extraction and graph-based analysis to better capture important details in the images. The model was tested on public datasets and showed higher accuracy and reliability than existing methods. It helps doctors by making disease diagnosis faster and more precise.
  14. Conditional Generative Adversarial Network Model for Conversion of 2 Dimensional Radiographs into 3 Dimensional Views
    This project develops a method to convert 2-D medical images like X-rays into 3-D views. It uses a specialized deep learning model that can show the organ from all angles. The system cleans and standardizes the images before processing, and it is designed to work even with noisy or unclear inputs. Tests on real hospital data show that the generated 3-D images preserve important details and match the quality of the original scans.
  15. Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis
    This project builds a smarter system to predict breast cancer early. It uses different types of patient data together, such as clinical details, gene information, and genetic variations. The system learns patterns from each data type using different deep learning models and then combines them. This combined model improves prediction accuracy compared to using a single data source.
  16. DeepCurvMRI Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimers Disease
    This project aims to detect Alzheimer’s Disease early using MRI brain images. The researchers first enhanced the images and then trained a deep learning model to recognize patterns linked to different stages of the disease. The model learned these patterns with very high accuracy. This approach could help doctors identify Alzheimer’s much earlier and more reliably.
  17. Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network
    This project focuses on detecting early-stage Alzheimer’s disease using patterns in people’s handwriting captured online. Since there is limited data available, the study uses a special type of artificial intelligence, called DoppelGANger, to generate realistic handwriting examples. These generated examples help train a neural network to recognize Alzheimer’s more accurately. The approach was tested on real handwriting data and showed much better results than existing methods.
  18. 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.
  19. Improved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet
    This project focuses on improving the detection of the prostate and prostate cancer from medical images. It combines a special type of convolution called deformable convolution with the U-Net model to better capture the changing shapes and sizes of the prostate. The method adapts to the features in the images, making segmentation more accurate. It also uses a training approach that transfers knowledge from healthy prostate images to cancer images, improving results even with limited data.
  20. 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.
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