A deep learning method for children's self-care problems classification using represent learning and focal loss

Abstract

Background

An accurate diagnosis of children's self-care problems significantly matters in the growth and development of children. However, various and extensive disorders make the self-care problems classification extremely complex and require much effort and time to solve.

Objective

To deal with the above challenge, a deep learning model is proposed to classify the children's self-care problems intelligently and precisely.

Method

The proposed deep learning model contains two sub-deep neural networks. The first sub-network employs a technology of representing learning named triplet loss. It aims to compress the dimensions of the feature of the children with self-care problems to extract the useful information and exclude the noise, in order to improve classification performance. The second sub-network utilizes a technology for handling the class imbalance problem called focal loss to further improve the classification accuracy.

Result

The experimental results show that the proposed deep learning model outperforms. The averages of accuracy, precision, recall, and F1 score can achieve 99.78%, 0.99, 0.99, and 0.99, respectively.

Conclusion

To the best of our knowledge, the proposed method achieves state-of-the-art results. That can significantly support the rehabilitation and growth of children with self-care issues. Furthermore, this study also provides a demonstration and experience of the application of the deep learning model in the healthcare field.