Medical Sign Recognition of Lung Nodules based on Image Retrieval with Semantic Feature and Supervised Hashing

Computer-aided diagnosis (CAD) technology can improve the efficiency of a physician diagnosing lung lesions, especially sign recognition, which is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Then, we translate the high-dimensional image features into compact binary codes with principal component analysis (PCA) and supervised hashing. Next, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Lung Computed Tomography (CT) Imaging Signs (LISS). The experimental results demonstrate our method substantially improves retrieval performance and can achieve 87.29%. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.