Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning

Background and objectives: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the insufficient and incomplete data issue since it is clin-ically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage.Method: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of insufficient and incomplete training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost -sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights.Results: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction.Contributions: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost -sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classifica-tion performance.

Automated summary

In this study, label distribution learning (LDL) and a cost-sensitive mechanism were used to predict the severity of pulmonary diseases. By generating a label distribution for each patient, the CT images were able to learn not only the information of the current day, but also the information of the neighboring days. The results showed that the proposed method achieved superior performance in predicting the severity of pulmonary diseases.