# [PERSON]'s Classification for Colon Polyps Assessment Using a Deep Learning Approach
## 2 Methodology  
Early detection of cancerous polyps allows for reducing the colon cancer incidence rates. High quality endoscopes with high-magnification or other imaging capabilities makes the classification of polyp Types easier. Nonetheless, these devices are not always available, therefore, it is important to develop methods agnostic to the colonoscopy devices to reduce costs and make these tests more affordable for all regions in the world where the access to special equipment to provide a good results is scarce. Following [PERSON]'s polyp classification [7], we decided to separate polyps between malignant and nonmalignant. Given that the first two categories are not associated with malignancy and Types III and IV have a low risk of containing carcinoma, we decided to group them as nonmalignant. Type V polyps otherwise has a 56% risk of malignancy, so we can confidently label that type as the only one malignant in our test environment [8]. These polyp types can be seen in Figure 1. Based on this, we can train an algorithm that will help in reduction of incidence ratio with a reduced need for intervention. This way specialists could have an additional tool improving the odds of a patient, and potentially reducing colorectal cancer incidence. For our training and testing dataset, experts associated to the University of Deusto collected and classified a set of 600 images into five different categories of pit patterns defined by [PERSON]. These images where collected from 142 patients in the Urduliz hospital and Biodonstia Health Research Institute with the required ethics committee approval. Polyp images were extracted from colonoscopy videos  
Figure 1: Examples of [PERSON]’s classification scale of Polyps ranging from I through V.  
recorded using different colonoscopy cameras and under multiple lighting conditions. The collected images ended up having one of the following resolutions: 576 \(\times\) 768, 576 \(\times\) 1047, 1024 \(\times\) 1280, 1048 \(\times\) 1232, 1072 \(\times\) 1440, 1080 \(\times\) 1350. To make our system general we included a preprocessing step, downscaling the images to a resolution of 150 \(\times\) 150, it should be noted that this process is not necessary in the deep learning model, but enable the system to work efficiently with less computing capacity. The number of images contained in each Kudos' category is presented in the first row of Table 1, due to the limited amount of images per category data augmentation techniques were applied to the dataset. In our experiments we applied rotation, horizontal and vertical shifting, shear, zoom, and mirroring, to the images leading to the number of records presented in the second row of Table 1.  
After checking the final data count, we merge the first four categories and treated them as non malignant polyps, while leaving Type V polyps as malignant for our experiments. We sampled equal quantities from each of the first four classes to obtain a similar amount of images compared to Type V images in order to have a balanced dataset. Before the rise of deep learning, researchers used to produce hand crafted transformations in order to feed the images in a model such as SVM. A modern convolutional neural network pipeline can be seen as a full feature extractor and a classifier join together in one model. In the previous model, this separation can be noticed where the convolutional layers get flatted and used in a dense layer. Our plan for classifying the polyps into malignant and non malignant consisted of an incremental approach based on ideas done in similar works such as [14]. We first built a small deep convolutional model to test if it could learn effective transformations in order to extract useful features for a robust polyp classification. We then compared this network with another one using a pre-trained feature extractor based on the VGG-16 neural network architecture [16]. We tested two situations with this model.