Contents I. Introduction
Lung cancer is not new to us, with silent symptoms in the early stages but outbreaks in the late-stage. According to statistics, about 80% of lung cancer patients are diagnosed when the disease is at a late stage with clinical symptoms such as cough, chest pain, shortness of breath, weight loss, etc. In 2017, lung cancer killed more people than breast, prostate, colorectal, and brain cancers combined [1]. The detection of small tumors may offer hope to patients with a treatment that combines surgery and chemotherapy. Routine screening with general tests or noninvasive imaging modalities like low-dose computed tomography (CT) aids in the early detection of lung nodules when they are small and show no evidence of metastasis. However, imaging diagnosis puts pressure on inexperienced doctors. Computer aided system with automatic tumor identification algorithms is really needed. These algorithms are categorized into two groups: those that employ handdraft features and those that use deep learning networks. For example, one approach is to use the Gabor algorithm to segment the image area as a fingerprint reference point or not [2]. These techniques can also be applied to clarify the tumor or non-tumor areas in medical images, however, due to the diverse and complex nature of medical images, using Gabor will speed up the treatment. image processing is slower, so we decided to use the most modern trends in the deep learning family to solve this problem. On the other hand, there are many groundbreaking studies applying deep learning networks to solve the problem of identifying tumors in the lung. Dou and colleagues [3] create three deep learning network designs (Archi-1, Archi-2, and Archi-3) on the base of the multi-context 3D convolutional network idea known as CUMedVis to overcome issues caused by variances in node size, shape, and geometrical data. These three designs are then combined with a weighted linear combination to provide the final classification result for the candidates. Ding [4] suggested a CNN-based pulmonary node identification method. Their technique use region-based CNNs to detect nodules on image slices and 3-D CNNs to decrease false positives. The approach was tested on the LUNA16 dataset and shown great sensitivity (94.4 %).
