Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning

Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously endangers human health and has high incidence and mortality worldwide. Therefore, an effective predictive model is required for COPD diagnosis. Given the limited data samples available in current COPD studies, we propose a method for diagnosing COPD based on transfer learning called balanced probability distribution (BPD) algorithm; this algorithm integrates instance-and feature-based transfers to improve the prediction accuracy of the model. First, instance-based cascaded transfer learning was used to initialize the weight distribution of the training data and obtain instances closer to the target domain. Second, the crossdomain feature filtering algorithm was adopted to filter irrelevant features, eliminate redundant features, and obtain the co-occurrence features of the source and target domains. Moreover, the remaining features were assigned different weights and transformed into the same space to reduce the distribution difference between the domains. Third, the BPD algorithm was used to balance the examples and the co-occurrence features from multiple disease source domains and construct a more suitable classification model of the target domain. Finally, the elastic network was used to further improve the generalization performance of the model. The experimental results show that the prediction effect of the BPD model is better than that of state-of-the-art methods and has strong generalization ability and robustness. We proved that our proposed BPD method works well in the COPD prediction model when the sample size is small. INDEX TERMS Balanced probability distribution (BPD) algorithm, chronic obstructive pulmonary disease (COPD), feature extraction, few-shot learning, transfer learning.

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

“…These parameters were not further utilized by the researcher to classify the COPD and pneumonia LS [ 20 ]. Nevertheless, the number of extracted features in some existing research works is less than the proposed technique but it can only identify a single pulmonary illness from LS analysis [ 6 , 12 , 18 , 25 , 26 ]. Details are presented in Table 8 .…”

“…The proposed technique has provided an efficient approach with outstanding classification accuracy. It has outperformed as compared to existing techniques on other multiple pulmonic pathologies from LS analysis due to its simple statistical features, low computation, and accuracy [ 6 , 7 , 12 , 20 , 22 , 24 , 25 , 28 , 29 ]. The performance analysis of the proposed diagnostic technique with existing pulmonic pathologies methods is shown in Table 9 .…”

“…If any symptom or feature is reported due to human error then the proposed system is unable to diagnose properly. It emerges the need for an automatic, economic, and non-invasive system for the diagnosis of COPD [ 25 , 26 , 27 ]. Another study is made for the identification of pneumonia and asthma subjects from cough sound analysis, specifically focused on the pediatric population.…”

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

“…For example, COPD is a common cause of wheezing, but asthma, bronchitis, laryngitis may also occur wheezing. Using multi-dimensional features to diagnose COPD has high accuracy and can save diagnosis and treatment costs to some extent [ 16 , 17 ]. Nevertheless, it is difficult to quickly diagnose the severity of COPD due to a large amount of information required and the need for experienced doctors.…”

“…Previously, most studies have used multi-dimensional features in the diagnosis of COPD. For example, QianWang et al used the transfer learning algorithm based on balanced probability distribution and instances to diagnose COPD, and the accuracy rate reached 95.2% [ 16 ]; Jun Ying et al utilized DBN to predict the exacerbation frequency of COPD, and the accuracy reached 91.99% [ 17 ]. Some studies diagnose COPD based on lung sounds, extract features through the short-time Fourier transform, wavelet transforms, or HHT, and then use an artificial neural network or deep learning algorithm for recognition and classification, achieving high accuracy.…”