Convolutional Neural Network Model Final Year Projects with Source Code

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption
    This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
  12. 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.
  13. Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
    This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
  14. A Smart Leaf Blow Robot Based on Deep Learning Model
    This project created a robot that can automatically collect fallen leaves. It uses a camera and a computer program to recognize leaves on the ground. The robot moves on wheels and directs a blower to gather the leaves into a bag. The system works in real time and can handle different types of leaves without human help.
  15. A Systematic Review of Facial Expression Detection Methods
    This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
  16. Fault Classification in Distribution Systems Using Deep Learning With Data Preprocessing Methods Based on Fast Dynamic Time Warping and Short-Time Fourier Transform
    This project focuses on detecting short-circuit faults in power systems more accurately and efficiently. It converts voltage and current signals into a time-frequency map and uses a neural network to identify fault types. The method also removes redundant data to make training faster and more reliable. Simulations show it achieves very high accuracy and reduces training time significantly.
  17. Heterogenous Social Media Analysis For Efficient Deep Learning Fake-Profile Identification.
    This project focuses on detecting fake social media accounts. It collects and analyzes data like posts, comments, images, videos, and user activities. The system uses deep learning to find patterns that show an account is fake. Tests show it can identify fake accounts with over 93% accuracy.
  18. Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network
    This project uses deep learning to identify patterns in electrical partial discharges from images. It combines two networks, CNN and LSTM, to improve detection accuracy. Data augmentation increases the number of training images. The proposed hybrid model achieves almost perfect accuracy in recognizing different PD types.
  19. Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma Patients
    This project aims to predict how glaucoma will affect a patient’s vision in the future. It uses a deep learning model that looks at past vision test results and eye scan images to make predictions. The system combines image analysis with previous test data to improve accuracy. It also identifies and handles noisy or unreliable data, making the predictions more reliable for monitoring glaucoma progression.
  20. Water Classification Using Convolutional Neural Network
    This project focuses on classifying different water sources using images. The researchers improved the images’ quality using special techniques to make textures and contrasts clearer. They then used a new neural network called WaterNet to identify the water types. Their method achieved 97% accuracy and performed better than existing popular models.
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