Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
It addresses costly and invasive diagnostic methods for lung diseases like cancer and COPD, though it appears incremental.
This study tackled lung disease diagnosis by using semi-supervised learning with MFCC+CNN on lung sound signals, achieving 92.9% accuracy, a 3.8% improvement over the baseline.
Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations. With the add-on semi-supervised modules, the accuracy rate of the MFCC+CNN model is 92.9%, an increase of 3.8% to the baseline model. The research contributes to the field of lung disease sound detection by addressing challenges such as individual differences, feature insufficient labeled data.