# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 4 Detection of CISs Based on Deep Learning Methods
### Classification  
#### 4.1.1 Fully-Supervised Classification  
CNN is the major approach of deep learning applied to lung CT CIS detection. The network comprises a convolutional layer, a pooling layer, and a fully connected layer. The shallow network comprises a convolutional layer and a pooling layer to extract the region of interest, whereas the deep network consists mostly of a fully connected layer to accomplish tasks such as logistic regression and classification. The strategy of using CNN is divided into two parts: off-the-shelf CNN and fine-tuning. Off-the-shelf CNN refers to the use of CNN models trained in other domains as feature extractors without retraining CNN models using medical images, and employing CNN features as supplemental information to existing manual features. Fine-tuning refers to pretraining CNN models on natural image datasets (COCO, ImageNet) and then conducting supervised model refining on medical image datasets. The training of CNN models requires a vast quantity of data. However, in the field of medical image processing, the premise of having a sufficient amount of well-labeled data for training may not hold, and direct training of models from scratch is prone to overfitting. Therefore, previous works employ off-the-shelf CNN; that is, the traditional characteristics  
Figure 3: Network frameworks used in deep learning methods: Fully-supervised classification sends the CT images to the convolutional neural network (CNN) for classification and outputs different CISs class; while semi-supervised classification addresses the problem of lack of dataset by using the generative adversarial network (GAN) to generate a large number of high-quality samples to expand the training data and then send them to the convolutional neural network for classification; image retrieval sends the images to the convolutional neural network to extract features and calculate the similarity between these features and the features of the image to be retrieved to return a collection of lesion images similar to the query image. of current medical imaging data are merged with the features extracted by CNN to identify signs. In 2019, [PERSON] [28] employed CifarNet as a CNN feature extractor, trained on the Cifar-10 dataset and then transferred to the cavity samples for fine-tuning, to identify cavity signs by merging CNN features with classical HOG and LBP features. Their work compared the classification impacts of CNN, HOG, LBP, and fused features, and the FS score was 0.0757, 0.1136, 0.1138, and 0.1472, respectively. Results demonstrate that the HOG and LBP features give extra relevant information, and that a fusion features model can outperform that of using any single feature. Furthermore, our technique greatly enhances the sensitivity (85% vs. 70% vs. 62%) of recognizing cavity signs compared to existing advanced cavity sign identification systems [29; 30]. The proposed feature fusion approach indicates that knowledge learned from various datasets rather than the training sample may provide an effective solution to the problem of limited well-labeled medical images. Due to the huge variances in different datasets, fusion features may not be able to accurately reflect the characteristics of the target field. Therefore, a natural idea is to fine-tune the obtained CNN model using the target field data to improve detection accuracy. [PERSON] [31] employed GGO samples to retrain the acquired CifarNet and trained multiple CNN models based on multiple-receptive-field, and then fine-tuned and merged multiple CNN models. The model fusion sensitivity reached 96.64%, which was considerably better than the classification performance of [PERSON] [32] (70%) and [PERSON] [33] (83%) after fine-tuning only a single CNN model. In [34], the Inception model replaced CifarNet and boosted the specificity of the model by 21.74%. While making great progress compared to previous approaches, the quantity of the dataset needed to train a high-capacity deep learning framework is quite large.