Convolutional Neural Networks Final Year Projects with Source Code

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

  1. 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.
  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 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.
  4. 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.
  5. A Stock Price Prediction Method Based on BiLSTM and Improved Transformer
    This project develops a new method to predict stock prices more accurately and reliably. It combines three advanced models—BiLSTM, Transformer, and TCN—to use their strengths in analyzing stock data. The method was tested on multiple stocks and consistently outperformed existing prediction approaches. It provides stable, precise forecasts without timing issues.
  6. 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.
  7. 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.
  8. Automated Segmentation of Brain Tumor MRI Images Using Deep Learning
    This project focuses on automatically identifying and separating brain tumors in MRI images. It uses advanced image processing and neural networks to remove unnecessary details and improve tumor detection. The method combines two deep learning models to make predictions more accurate and complete. Tests show it achieves very high accuracy, precision, and reliability in identifying different tumor regions.
  9. Bit-Plane and Correlation Spatial Attention Modules for Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It improves existing deep learning methods by adding a special attention model that focuses on the most important parts of plant images. The model detects disease areas more accurately and achieves very high accuracy on public plant disease datasets. The experiments show it works better than many commonly used methods.
  10. Classification of Liver Fibrosis From Heterogeneous Ultrasound Image
    This project studies how artificial intelligence can help doctors diagnose liver problems using ultrasound images. The researchers found that AI models work well on images similar to those they were trained on but perform worse on images from different machines. They also explored ways to reduce errors caused by differences between machines and improved classification by combining similar categories. The work highlights the need for AI that performs reliably across different ultrasound devices.
  11. DeepSkin: A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using computer-based methods. It uses images of different skin lesions from a large dataset to teach a computer to recognize cancer types. Advanced deep learning models, like CNNs with DenseNet169 and ResNet50, are applied to improve accuracy. The goal is to help doctors identify skin cancer more reliably and quickly.
  12. EfficientNetB3-Adaptive Augmented Deep Learning AADL for Multi-Class Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It uses advanced deep learning models that have already been trained on large datasets to recognize 52 types of diseases and healthy leaves. The study tested several models and found that one called EfficientNetB3-AADL gave the most accurate results, correctly identifying diseases 98.7% of the time. This approach can help farmers quickly and accurately detect plant diseases to protect crops.
  13. 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.
  14. FieldPlant A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning
    This project focuses on improving the detection of plant diseases using images taken directly from farms. Researchers created a new dataset called FieldPlant, with over 5,000 real-field images carefully labeled by plant experts. They tested modern deep learning models on this dataset and found that these models performed better than when trained on previous datasets. The goal is to help farmers detect diseases more accurately and reduce food waste.
  15. 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.
  16. Human Behavior Recognition Based on Multiscale Convolutional Neural Network
    This project focuses on recognizing human actions in videos using a smarter neural network. It improves how the network looks at both the overall and local details in each video. The method splits videos into segments, extracts important behavior information, and combines it over time to understand the full action. Tests show it is accurate, faster, and more efficient than older methods.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
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