Automatic classification algorithms are an important component of expert decision support systems that are used in a number of medical applications including diagnostic radiology and disease detection. This study proposes a deep learning-based framework for medical image classification using wavelet features. Convolutional neural networks are incorporated to discover informative latent patterns and features from a set of X-ray images pertaining to human body parts. The features are then passed to a classifier for labelling the respective X-ray images. The experimental results show that the low-pass filter wavelet-based convolutional model outperforms the original convolutional network and some models for classifying X-ray images. The performance of the proposed method implies that it can be implemented effectively in practice for disease detection using radiological images.
|Name||IEEE International Joint Conference on Neural Networks (IJCNN)|
|Conference||2020 International Joint Conference on Neural Networks|
|Abbreviated title||IJCNN 2020|
|Period||19/07/20 → 24/07/20|
- Convolutional neural network
- deep learning
- medical imaging