# Colorectal Cancer Detection Tool Developed with Neural Networks
## 2 Materials and Methods
### The Datasets
The percentage at which it is recommended to do the colonoscopy would be around 20%, just to be sure, since early detection gives the highest chance of survival, but at around 35% the doctor should perform it. There are 3 levels of importance for each of the variables. The ones in the third category, which have less impact on the score, are not going to be analyzed in more detail. In Table 3 the impact of the main input variables is presented. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Total Patients** & **Healthy Patients** & **CRC Confirmed Patients** \\ \hline Initial & 300 & 170 & 130 \\ \hline Augmented & 700 (600 + 100) & 376 (330 + 46) & 324 (270 + 54) \\ \hline \end{tabular}
\end{table} 
Table 1: Class distribution table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Min. Possible Value** & **Max. Possible Value** & **Min. Healthy Value** & **Max. Healthy Value** & **Importance Level** \\ \hline Age & 0 & 120 & Nan & Nan & Nan \\ \hline Medical History & 0 & 1 & 0 & 0 & Nan \\ \hline Albumin & 0.5 & 10 & 3.5 & 5.2 & 2 \\ \hline Direct Bilirubin & 0 & 30 & 0 & 0.52 & 3 \\ \hline Total Bilirubin & 0 & 30 & 0.1 & 1.2 & 3 \\ \hline Creatinine & 0 & 10 & 0.67 & 1.17 & 3 \\ \hline Alkaline Phosphatize & 50 & 1000 & 98 & 279 & 2 \\ \hline Gamma GT & 0 & 1000 & 11 & 50 & 2 \\ \hline Glycemia & 20 & 500 & 70 & 115 & 3 \\ \hline GOT & 0 & 500 & 0 & 37 & 2 \\ \hline GPT & 0 & 500 & 0 & 40 & 2 \\ \hline Kalium & 0 & 10 & 3.5 & 5.4 & 3 \\ \hline Total Protein & 0 & 50 & 6.2 & 8 & 2 \\ \hline Natrium & 0 & 500 & 130 & 145 & 3 \\ \hline Time Quick & 0 & 50 & 13 & 17 & 3 \\ \hline IP & 0 & 500 & 70 & 130 & 3 \\ \hline INR & 0 & 50 & 0.84 & 1.1 & 3 \\ \hline Urea & 0 & 500 & 18 & 48 & 3 \\ \hline Iron & 0 & 500 & 65 & 175 & 1 \\ \hline Leucytes & 0 & 500 & 4 & 10 & 3 \\ \hline Basophiles & 0 & 500 & 0 & 1 & 3 \\ \hline Neutrophnis nr. & 0 & 10 & 2 & 7.5 & 3 \\ \hline Neutrophlines percentage & 0 & 100 & 30 & 75 & 3 \\ \hline Eosinophiles & 0 & 100 & 1 & 4 & 3 \\ \hline Lymphocytes & 0 & 100 & 20 & 40 & 3 \\ \hline Monocytes & 0 & 100 & 2 & 10 & 3 \\ \hline Hemoglobin & 0 & 100 & 14 & 18 & 1 \\ \hline MCV & 0 & 500 & 80 & 96 & 1 \\ \hline Erythrocytes & 0 & 500 & 4.5 & 6.3 & 2 \\ \hline MCH & 0 & 50 & 26 & 34 & 2 \\ \hline MCHC & 0 & 100 & 31 & 37 & 2 \\ \hline RDW (rdw-cv) & 0 & 100 & 8.5 & 11.5 & 3 \\ \hline RDW (rdw-sd) & 0 & 50 & 35 & 36 & 3 \\ \hline Hematocrits & 0 & 100 & 40 & 54 & 1 \\ \hline Thrombocytes & 0 & 1000 & 140 & 440 & 1 \\ \hline Associated Pathology & 0 & 10 & 1 & 10 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input variables. Next, the colonoscopy imaging diagnosis method is considered. For this model, the Kvasir dataset [35] is considered. It has several folders with different images that could be used for the detection of colorectal cancer. The colonoscopy images from it are used for the training of CNN. There are 2 cases that are analyzed. In the first case, 500 healthy images and 500 images that were previously diagnosed as positive for CRC are used. For the second case, 500 images of ulcers are added. Figure 1a presents a colonoscopy image of a colon positively diagnosed with CRC. In the middle of the image, the polyp can be seen as a tumor. Figure 1b is what a healthy colon looks like.