X-rays lung disease screening algorithm based on the fusion of improved variational autoencoder and radiomics

Computer-aided technologies have achieved excellent results in lung diseases screening. However, most of the existing studies models the known type of disease, which can lead to the risk of misdiagnosis and missed diagnosis to the unknown type of disease. In addition, current research primarily focuses on achieving high accuracy without solving the problems of misdiagnosis and missed diagnosis, making it difficult to apply in clinical practice. This paper proposes a more clinically applicable goal for computer-aided pulmonary disease screening by combing the doctor’s diagnosis process: to ensure a zero missed diagnosis rate while reducing the misdiagnosis rate. To achieve the screening goal mentioned above, this paper proposes an improved variational autoencoder network based on one-class classification for preliminary screening of lung diseases and extraction of deep encoding features from X-ray images. Then, by combining the complementary advantages between radiomics features based on doctors’ experience and deep learning features, an ensemble learning model is constructed to complete the final lung disease screening. The method in this study improved the classification performance and reduced the missed diagnosis rate with only normal X-ray images involved in training. Experimental results show that the proposed method achieves an AUC value of 0.9848±0.0023 and reduces the misdiagnosis rate to 0.1498±0.0057 when the missed diagnosis rate is zero, further validating that the method can effectively achieve the pulmonary diseases screening goal of this paper. At the same time, we compared the screening results of the ensemble model and individual deep learning model. We found that the results of the ensemble model was significantly better than the deep learning model, which further highlights the effectiveness of integrating doctors’ experience.