Abstract
Pectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these indexes cannot represent the whole information of chest CT images and may associated with significant error due to the individual differences. To overcome these limitations, this paper developed a computer-aided diagnosis (CAD) system based on the convolutional neural network (CNN) to automatically learn discriminative features and classify PE images. We also adopted block-wise fine-tuning methods based on the transfer learning strategy to reduce the potential risk of overfitting caused by limited data and experimentally explored the best fine-tuning degree. Our method achieved a high level of classification accuracy with 94.76% for PE diagnosis. Furthermore, we proposed a majority rule-based voting method to provide a comprehensively diagnostic results for each patient, which integrated the classification results of the whole thorax. The promising results support the feasibility of our proposed CNN-based CAD system for automatic PE diagnosis, which paves a way for comprehensive assessments of PE in clinics.