Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine learning approaches to automatic diagnostic are being proposed to this task. As in most health problems, the imbalance between examples and classes is predominant in this problem and affects the performance of the automated solution. In this paper, we address the classification of heartbeats images in different cardiovascular diseases. We propose a two-dimensional Convolutional Neural Network for classification after using a InfoGAN architecture for generating synthetic images to unbalanced classes. We call this proposal Adversarial Oversampling and compare it with the classical oversampling methods as SMOTE, ADASYN, and Random Oversampling. The results show that the proposed approach improves the classifier performance for the minority classes without harming the performance in the balanced classes.
- Towards Optimizing Convolutional Neural Networks for Robotic Surgery Skill Evaluation
- A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
- Convolution Optimization in Fire Classification
- Kutralnet: A Portable Deep Learning Model for Fire Recognition
- Multi-human Fall Detection and Localization in Videos