## 3 Present
### CAD in Lung Cancer Diagnosis
#### 3.1.1 Algorithms Proposed for Nodular Detection  
We generally find two types of CAD systems for the detection of nodules in a CT image: conventional methods and Deep Learning based methods. Conventional methods rely heavily on hand-crafted features that are recognized in the image and thus can affect the generalizability of the classifying algorithms used (which usually classify the finding either as a nodule or a non-nodule finding). The conventional CAD models for nodular detection collected in Table 1 are three: [PERSON] et al. [8], [PERSON] et al. [9], and [PERSON] et al. [10]. Although they obtain good results, their performance could decrease sharply when using larger databases, since all three use a small number of scanners, and two of them make use of hospital databases that are not publicly available. The main reason why these models may experience a decrease in performance would presumably be the high variability of the nodules and the limitations of the human-established features, which are both disadvantages inherent to this type of model. On the other hand, we have the DL-based models which are the rest. The most important difference between standard methods and these ones is that the latter, by supplying a large amount of medical imaging data, automatically learns the features that differentiate the relevant findings from what we would call normal. The nodule detection takes place in a process that usually includes establishing a database with correctly labeled results (according to the standard of reference) that the model is expected to obtain, choosing the learning type that best suits our model based on the input and output we are able to provide to the model, and dividing the database into subsets for training, validating and testing the model. Regarding the DL models that appear in the table, [PERSON] et al. [11] propose a 2D convolutional neural network (CNN) model in contrast to most modern models, which use 3D CNN. [PERSON] et al. [12] propose the use of Amalgamated-CNN. [PERSON] et al. [13] use a DL-CNN model trained in a private database, using the results of the double reading of radiologists combined with the CAD model as the Standard of Reference, that is, a panel of expert radiologists that have the complementary information of the model itself. This DL algorithm demonstrated superior sensitivity, and therefore, detected more nodules than radiologists, as well as comparable performance in distinguishing solid nodules from ground glass opacity nodules, with the disadvantage of a high false positive rate. [PERSON] et al. [14] use a U-Net-like 3D-Faster R CNN algorithm and a 3D classifier to reduce false positives. [PERSON] et al. [15] make use of MIP images (Maximum Intensity  
Figure 2: (**a**) Target of CAD studies about Lung Cancer included in this manuscript. (**b**) Algorithms used for Lung Cancer studies included in this manuscript. Projections) in their model and conclude that this achieves notable benefits when using CNN to detect lung lesions, especially the smallest ones. [PERSON] et al. [16] affirm that the success of their model comes from three key points: that the model is based on 3D-DCNN, the use of a 3D-region proposal network (RPN) with a U-Net type structure and subsequent reduction in FP, and the use of Residual Networks Squeeze and Excitation (SE) type to accelerate the training process and the accuracy of nodular detection. [PERSON] et al. [17] make use of a DL-CNN model that takes into account features such as shape and texture and propose to study the implementation of rather traditional CAD features such as curvature or entropy in future works, as well as clinical ones (e.g., age, sex, race, tobacco, etc.). Finally, the model by [PERSON]. et al. [18] represents an exception compared to the rest of the studies included in the table since they propose a 2D-DCNN model (named LdcNet) that classifies the candidate findings into nodule and non-nodule but requires the prior detection of these findings which they obtain from the LIDC database.