Denoising Autoencoder Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Denoising Autoencoder 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.

Denoising Autoencoder Final Year Projects

An End-to-End Deep Learning Framework for Real-Time Denoising of Heart Sounds for Cardiac Disease Detection in Unseen Noise

This study focuses on cleaning heart sound recordings that are often disturbed by environmental and body noises. The researchers developed a deep learning model called LU-Net that removes these unwanted noises from heart sounds. The model was tested on both synthetic and real noisy recordings and performed better than existing methods. This approach can help doctors in busy or low-resource hospitals get clearer heart sound signals, improving the detection of heart diseases.
Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning

This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.

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Denoising Autoencoder Project Synopsis & Presentation

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Denoising Autoencoder Final Year Projects with Source Code