# Using Double Convolution Neural Network for Lung Cancer Stage Detection
## 1 Introduction
In [16,17], the authors used MATLAB algorithms to pre-process the image and remove such artifacts. The authors of [18,19] performed experiments with 30 and 50 lung cancer images and, based on statistical analysis, they determined the threshold to remove noise in the image. The threshold detection technique is used in [20] to remove noise in breast cancer RGB (color) images. Another method of threshold detection to reduce artifacts in an image is the [PERSON]'s method of binarization. In [21,22], the authors first used the [PERSON]'s method to determine the threshold and then applied statistical analysis to adjust and optimize the threshold value. Using this optimized threshold value, pixel peaks are removed and images have less noise (artifacts). The authors used one- and two-stage [PERSON]'s thresholding methods to compare them with 2D and 3D images. Unlike the work in [16, 17, 18, 19, 20, 21, 22], our threshold detection is defined as determining the Tx stage where our algorithm can detect the possibility of cancer. This paper is organized as follows. Section 2 presents state-of-the-art solutions to the main problem in our research, which is classification of medical images and detection of cancer. We present how other types of medical images are classified and present other systems for medical image classification regarding lung cancer. Section 3 presents how the data were prepared as well as the defining, training and testing of our DNN. It also presents CT images of the different layers and how they communicate to output the best result. Section 4 presents an additional dataset of 35 patients used to determine the cancer stage in T 2, 3 and 4. The cancer stage detection and the results are presented in Section 5. Section 6 concludes the paper. number of medical images is difficult to obtain, other methods can be used to train the DNN. In [23], the authors used active learning to help with the dataset, that is, to help with selecting and classifying the images before training. They used multistage training scheme to overcome the overfitting problem, which means that they started with a smaller dataset and reduced it to the point where there is no overfitting. For each step, they predicted the amount of data they needed to send the DNN and measured if and when overfitting happens. To train the network with \"heavy\" multimedia, one needs to have large set of input nodes to pass the information through the network. In [24; 25], the authors used extremely large Computer-Aided Detection (CAD) 3D images of lung cancer to provide the classification. To achieve this, they used U-Net LUNA 16 labeled data nodules to pass throughout the network. They had smaller pieces of the images already divided into nodules (pieces) that were pre-labeled as malignant or not. This way, the entire image is not taken into consideration, but the small nodules that are directly mapped to the nodes of the network. Images can be classified (using CDNN) in more ways, not just into piles of cancerous or non-cancerous. In [26; 27], the authors used fluorodeoxyglucose positron emission tomography (FDG-PET) images to determine the Tx stage of the cancer. They defined four piles of T1-T4 stages of cancer and determined the outcome of the classification. As an extended research [26], the authors of [27] additionally used CAD images and compared the results with FDG-PET. We propose a lung cancer medical image classifier that is based on a Convolutional Deep Neural Network. To train and test our system, we used CT images of lungs that were previously classified by medical specialists and put into piles of yes/no (yes, the patient is diagnosed with lung cancer; and, no, the patient is cancer-free). Similar to [PERSON] and [PERSON] in [23], we pre-classified the images, but, in our case, we pre-classified them into groups of slice images taken from the same angle of the lung from different patients from our training dataset.