Convolutional Neural Network (CNN) projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Convolutional Neural Network (CNN) projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Convolutional Neural Network (CNN) Projects

Latest Convolutional Neural Network (CNN) Projects

  1. A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
    This project aims to develop an intelligent deep learning system for classifying diabetic retinopathy based on fundus images. It focuses on identifying both early and severe retinal lesions that previous models often ignored. The approach combines GoogleNet and ResNet with an adaptive particle swarm optimizer to improve feature extraction. The extracted features are then tested with machine learning models such as random forest and SVM. The hybrid model achieves high accuracy and shows strong potential for improving diagnosis of DR severity levels.
  2. A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection
    The project aims to enhance security and privacy in Industrial Internet of Things networks by detecting malicious activities accurately. It focuses on improving traditional machine learning and deep learning methods that struggle with imbalanced and repetitive network data. The approach uses a self-attention-based deep convolutional neural network to monitor network behavior. It also applies data cleaning and feature filtering techniques to reduce redundancy and improve model performance. The system is tested on benchmark datasets and compared with existing models to show its effectiveness.
  3. Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling – A Critical Review
    The project aims to predict chronic diseases early using machine learning. It focuses on improving medical data quality by handling missing values, outliers, feature selection, normalization, and imbalance. The goal is to choose the best machine learning methods that give high accuracy and reliability. The study also reviews existing research and highlights challenges and future directions for better prediction performance.
  4. Machine Learning Approaches for Power System Parameters Prediction – A Systematic Review
    The main objective of this project is to improve prediction accuracy in power system networks. It aims to predict load, voltage, and frequency using machine learning models. The project focuses on using network topology behavior as input instead of isolated bus data. It tests different models like regression, decision tree, and LSTM. The goal is to provide better planning and reliability for dynamic and interconnected power systems.
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