Machine learning GAN
ENTERPRISE
Lancer:
<= 2 resources OR <=3 weeks effort OR <=$3000 USD
Growth:2 - 4 resources AND upto 4 months OR
upto $30000 USD
Enterprise:Everything else
(Development)
Project Description
The Machine Learning GAN project focused on developing a robust system for classifying time series medical data, denoising datasets using GANs, and ensuring model adaptability across varied input formats. The solution utilized CycleGAN to clean noisy signals from heart and respiratory sound datasets. Additionally, the project explored transfer learning strategies to enable classification models to perform accurately on new or altered data sources.
Scope Of Work
The client engaged Oodles to build machine learning models that perform three core functions. We trained classification models on time series medical data to accurately detect health patterns. We applied Generative Adversarial Networks (CycleGAN) to effectively denoise audio datasets, enhancing data quality for analysis. We also evaluated and improved the transferability of trained models, enabling them to adapt efficiently across different input datasets.
Our Solution
We delivered a machine learning pipeline tailored to the client's needs:
- Time Series Classification: Trained deep learning models to accurately classify heart and respiratory sound data using annotated time series records.
- CycleGAN-Based Denoising: Implemented CycleGAN to remove noise from medical audio inputs, improving the signal-to-noise ratio and model accuracy.
- Dataset Integration: Integrated the CirCor and Kaggle respiratory datasets into the training pipeline, ensuring high-quality inputs for model training and evaluation.
- Transfer Learning Strategy: Evaluated model performance on cross-domain datasets and applied fine-tuning techniques to boost transferability.
- Result Replication: Successfully replicated experimental results outlined in the research papers, validating our models with benchmark datasets.