Convolutional Neural Network Final Year Projects with Source Code

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

Convolutional Neural Network Final Year Projects

  1. A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images
    This project focuses on identifying how severe a brain tumor is using MRI images. It uses a light and fast deep learning model to classify tumors into low-grade or high-grade groups. The model is trained on public medical datasets and data from a local hospital. It shows very high accuracy compared to other popular deep learning models.
  2. 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.
  3. A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques
    This project focuses on detecting harmful Android apps without breaking app rules or licenses. It uses multiple detectors that check apps at different stages, before and after installation. The method is faster, uses less energy, and makes fewer mistakes than existing solutions. It helps keep Android devices safer from malware efficiently.
  4. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets
    This project studies public reactions to the recent monkeypox outbreak by analyzing social media posts. Researchers collected over 500,000 tweets and labeled them as positive, negative, or neutral. They tested many machine learning models to find the best way to predict public sentiment. The study found that a model using TextBlob, lemmatization, CountVectorizer, and SVM gave the most accurate results, helping health authorities understand public concerns.
  5. A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines
    This project focuses on detecting fake or misleading online reviews that can mislead customers and harm businesses. It uses a machine learning model called Weighted Support Vector Machine combined with an optimization algorithm called Harris Hawks Optimization to improve accuracy. The method works for multiple languages, including English, Spanish, and Arabic. The system was tested with different techniques and datasets, achieving high accuracy in identifying spam reviews, especially during the COVID-19 pandemic when online reviews increased dramatically.
  6. A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection
    This project focuses on detecting harmful content on social media. It uses machine learning methods to classify online messages as hateful or not. The study tests two models on multiple datasets and improves their accuracy using nature-inspired optimization techniques and fuzzy logic. This approach helps the system better understand the meaning behind the text.
  7. A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance
    This project focuses on detecting rumors on social media more accurately and quickly. It looks at both the hidden meaning in posts and the opinions of users who comment. The system gives more importance to trustworthy users and learns patterns over time. Tests show it can detect rumors faster and better than existing methods.
  8. 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.
  9. A Review of Methodologies for Fake News Analysis
    This project reviews research on detecting fake news, which is becoming more important as false information spreads online. It studies how fake news can be analyzed by looking at its knowledge, style, source, and how it spreads. Detection methods are either manual, using experts or crowds, or automatic, using machine learning. The study suggests that machine learning works well and plans to explore Bayesian methods for faster and more flexible detection in the future.
  10. 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.
  11. A Swin Transformer-Based Approach for Motorcycle Helmet Detection
    This project focuses on automatically detecting whether motorcyclists are wearing helmets using video cameras. It uses advanced computer vision and machine learning techniques to identify helmets even in complex situations and with multiple passengers. The method combines a transformer model with other neural network tools to improve accuracy. Tests show that it works better than existing detection systems.
  12. A Systematic Literature Review on Multimodal Machine Learning Applications Challenges Gaps and Future Directions
    This project reviews how machine learning can use multiple types of data together, like images, text, and audio, to solve real-world problems. It studies recent research on key challenges, such as combining, translating, and aligning these different data types. The authors analyzed over 1000 articles to identify trends, gaps, and progress in this area. This work helps researchers understand the current state of multimodal machine learning and plan future studies.
  13. A Systematic Review on Federated Learning in Medical Image Analysis
    This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI.
  14. 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.
  15. AMSeg A Novel Adversarial Architecture Based Multi-Scale Fusion Framework for Thyroid Nodule Segmentation
    This project focuses on automatically detecting and outlining thyroid nodules in ultrasound images. The researchers developed a new deep learning method that can identify nodule boundaries even when the tissue is blurry or uneven. Their system, called AMSeg, performs better than existing methods and can replace manual segmentation. This could make thyroid disease diagnosis faster and more accurate in clinical settings.
  16. 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.
  17. An Efficient Transfer and Ensemble Learning Based Computer Aided Breast Abnormality Diagnosis System
    This project focuses on improving early detection of breast cancer using computer-based tools. The researchers used advanced deep learning models, including EfficientNet, to analyze mammogram images. They combined multiple models together to make predictions more accurate and reliable. Their system achieved high accuracy in identifying both the type of abnormality and the disease itself.
  18. 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.
  19. BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
    This project develops a computer system that can automatically detect breast cancer from ultrasound images. It uses artificial intelligence to tell apart dangerous tumors from harmless ones. The system also explains its decisions using medical features that doctors rely on. Tests show it improves diagnosis accuracy and helps doctors understand its reasoning.
  20. 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.
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