Convolutional Neural Networks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Convolutional Neural Networks ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year 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 Networks Projects for MTech, MS & PhD Students

Latest Convolutional Neural Networks Projects

  1. CLADSI: Deep Continual Learning for Alzheimer’s Disease Stage Identification Using Accelerometer Data
    This project uses motion sensors to monitor the movements of Alzheimer's patients. A smart computer model, called a CNN, studies this data to detect the stage of the disease. The model can keep learning as new data comes in, without needing all past data. This helps doctors track patients continuously and supports better care.
  2. MASA-TCN Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
    This project focuses on understanding emotions from brain signals recorded by EEG. The researchers created a new model called MASA-TCN that can both predict exact emotional levels and classify emotions into categories. The model looks at patterns across different brain regions and over time to better detect subtle emotional changes. Tests show it performs better than previous methods on standard datasets.
  3. Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography A Numerical Investigation
    This project focuses on a new way to measure electrical properties of tissues using MRI without surgery. The method uses a series of neural networks that combine physics knowledge with data to improve the measurements. It was tested on brain models and produces accurate results much faster than traditional methods.
  4. A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images
    This project focuses on detecting fluid-filled cysts in the retina, which can cause vision problems. It uses optical scans of the eye and a deep learning model to automatically find and outline these cysts. The system helps doctors save time and improves the accuracy of diagnosis. The proposed method performed better than existing techniques on a standard dataset.
  5. Multimodal Non-Small Cell Lung Cancer Classification Using Convolutional Neural Networks
    This project focuses on detecting and classifying lung cancer at an early stage. It uses multiple types of biological data together, rather than just one. Advanced deep learning models are applied to these data to improve accuracy. The results show high success in identifying different lung cancer subtypes, which can help doctors choose better treatments.
  6. A Comprehensive Joint Learning System to Detect Skin Cancer
    This project focuses on detecting skin diseases early using computer algorithms. It combines two methods to analyze skin images and identify different types of skin conditions. The system is trained on a public dataset and can recognize multiple skin diseases with high accuracy. Results show it works better than individual methods alone.
  7. 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 analysis and deep learning to improve accuracy. The method combines two neural networks to give more complete and precise results. Overall, it achieves very high accuracy in detecting different parts of tumors.
  8. CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System
    This project improves smart transportation systems by combining cloud computing with edge devices. It uses a deep learning model called CNN, optimized with a special algorithm to make it faster and smaller. The model can quickly and accurately recognize traffic signs and objects from cameras. Tests show it works better than existing methods with very high accuracy.
  9. Coronary Artery Disease Classification With Different Lesion Degree Ranges Based on Deep Learning
    This project studies how artificial intelligence can help doctors analyze heart artery images. The researchers used deep learning to automatically classify parts of these images as having artery blockages or not. They tested several AI models and measured how accurate they were depending on how severe the blockages were. The results show that AI can be very accurate, but smaller blockages are harder to detect.
  10. DeepSIG A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification
    This project develops a smart system called DeepSIG to identify types of radio signals. It combines three different AI models to look at signals in multiple ways, like sequences, images, and graphs. The system learns from all these views together to make more accurate predictions. Tests show it works better than using any single method, especially when only a few signals are available.
  11. RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification
    This project focuses on detecting harmful software that can attack computers and networks. It uses artificial intelligence, especially deep learning, to analyze large amounts of data and identify malware. The researchers designed a new system called RMDNet that can classify different types of malware accurately. Tests show it works better than existing methods on several malware datasets.
  12. A High-Level Synthesis Library for Synthesizing Efficient and FunctionalSafe CNN Dataflow Accelerators
    This project focuses on making deep learning models run faster and more efficiently using special hardware. It allows users to turn CNN models from Python frameworks like TensorFlow or PyTorch into hardware designs automatically. The system improves speed and energy use while keeping the design process simple. It also adds safety and better data handling features for reliable performance.
  13. A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages
    This project focuses on identifying the different growth stages of pomegranates using images. It uses machine learning to recognize stages like bud, flower, and ripe fruit. The model helps farmers know the exact stage of their crops early. This can improve yield, quality, and reduce losses from pests or diseases.
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At UniPhD, we provide complete guidance and support for Convolutional Neural Networks ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

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Convolutional Neural Networks Research Support for PhD Scholars

UniPhD offers advanced Convolutional Neural Networks research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

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