Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
University of Bern · University Hospital of Bern
Abstract
Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size…
Citation impact
- FWCI
- 124.05
- Percentile
- 100%
- References
- 51
Authors
5Topics & keywords
- Convolutional neural network
- Artificial intelligence
- Computer science
- Pattern recognition (psychology)
- Computer-aided diagnosis
- Honeycombing
- Deep learning
- CAD