# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 4 Detection of CISs Based on Deep Learning Methods
### Image Retrieval  
With the remarkable success of CNN in the field of medical imaging, the natural question is whether we can apply CNN to the field of image retrieval. The foundation of the sign recognition approach based on image retrieval consists of the three steps of image feature extraction: similarity calculation, feature indexing, and dimensionality reduction. Due to the high dimensionality of the image features derived in the feature extraction stage, vast storage space is needed to preserve the features of images in the retrieval process in the face of massive medical images, which takes a lot of time to retrieve. Therefore, it is required to construct an index of characteristics to ensure efficient access. The hash image retrieval method maps the image features from the high-dimensional space to the low-dimensional binary space through some mapping relationship, and expresses the distance similarity coefficient between the query image and the database image in terms of the Hamming distance between the binary codes. Using hash image retrieval can allow recovery of images quickly and precisely, and may considerably minimize the storage space, which is extensively utilized in the field of medical image retrieval. Traditional medical image retrieval systems generally extract visual features such as shape, texture, and grayscale from images and then return a collection of lesion images similar to the query image by calculating the similarity between these aspects and the features of the image to be retrieved. The fundamental benefit of image retrieval methods based on deep learning is the integration of CNN with hash coding to increase the performance of image retrieval. In 2017, [PERSON] [40] used CNN to automatically extract image features, added a hash coding layer on the CNN, and created a deep-learning-based hash network model to accomplish image retrieval based on lung CT signs. Compared to the standard image retrieval method, the retrieval performance was enhanced by 2-5% on average. In 2021, [PERSON] [41] used a 3D ResNet network instead of the CNN and optimized the hash codes by the alternative minimization regular optimization approach, enhancing the average impact of the model by 5-8%. The proposed method illustrates that the selection of deep neural networks can increase the performance of image retrieval. In the same year, [PERSON] [42] proposed the YOLOv4 object detection model to implement the retrieval of lung CT signs, which compressed the retrieval time to 0.1 ms without affecting the retrieval accuracy. This study further established that a deep neural network with better performance plays a significant role in increasing retrieval performance. In addition, the similarity measure between images is also a vital step in determining retrieval performance. In order to retrieve comparable signs more effectively, [PERSON] [43] presented a multilevel similarity measure approach. Different from traditional approaches that only compute visual or semantic similarity, the suggested method is capable of computing level-specific similarity and optimum cross-level complementary similarity. It can attain roughly 80% accuracy and requires only 3.6 ms for the retrieval procedure. In summary, different deep neural networks combined with different hashing algorithms have distinct implications on retrieval accuracy and efficiency. Determining the ways in which to select effective deep neural networks for feature extraction and how to integrate deep learning with more efficient hashing algorithms to increase retrieval per formance remain the focus of future research in the field of image retrieval based on deep learning. Furthermore, the majority of the cited research above focus on the categorization of lung nodules or lung diseases. The recognition or categorization of signs is merely one stage in retrieval, rather than the ultimate aim of the research. More emphasis should be placed on the end-to-end identification or classification of signs.