Affiliations: [a]
Department of Computer Science, Electronic Engineering and Mathematical Science, Western Norway University of Applied Sciences, Bergen, Norway
| [b]
Department of Mathematics & Computer Science, Brandon University, Brandon, Canada
| [c]
Research Centre for Interneural Computing, China Medical University, Taiwan
Correspondence:
[*]
Corresponding author. Jerry Chun-Wei Lin, Department of Computer Science, Electronic Engineering and Mathematical Science, Western Norway University of Applied Sciences, Bergen, Norway. E-mail: [email protected].
Abstract: Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms.
Keywords: Meta-learning, transfer learning, feature extraction, classification, segmentation
