## 2 Related Works  
The remaining problem of existing lung cancer diagnosis systems is that they are capable of detecting tumor nodules only. As there are numerous nodule-like lesions in the lungs, it is still challenging to detect real tumor nodules. Most current CADs thus try to reduce the number of false positives, which can hinder the detection of real nodules. This means that current CADs defer staging assessments. Even though there have been a lot of studies on the computerized detection of tumor nodules, we found a paucity of literature on staging. In 2014, [PERSON] and [PERSON] [10] proposed an automatic tumor staging system from chest CT scans using marker-controlled watershed and Support Vector Machine (SVM). As a pre-processing method, they smoothed the input CT images with a Gabor filter, and then possible tumor nodules from the smoothed CT slices were segmented using marker-controlled watershed. Next, three geometric features--area, perimeter and eccentricity--were extracted and fed into the Support Vector Machine (SVM) for classification. Their method produced diagnosis results in four stages (T1, T2, T3, and T4), and they evaluated 40 CT scans from the National Institute of Health/ National Cancer Institute (NIH/NCI) Lung Image Database Consortium (LIDC) dataset. However, the evaluation assessments were not described in detail. Similarly, [PERSON] et al. (2015) [11] applied marker-controlled watershed for lung tumor detection and staging. Unlike [10], they used the sharpening method to enhance the input CT slices and then five features--area, perimeter, eccentricity, convex area and mean intensity--were extracted. They selected the true tumors based on an empirical threshold value of area. After that, staging was conducted by four alternative classifiers: Support Vector Machine (SVM), Naive Bayes Multinomial classifier (NBM), Naive Bayes Tree (NB tree), and Random Tree. Their experiment was validated using 200 CT slices from Regional Cancer Centre Trivandrum, and an accuracy level of 94.4% was obtained by the Random Tree classifier. Nevertheless, tumor segmentation based on the marker-controlled watershed is hard to fully automate, and it has a low guarantee in detecting any shape and size of nodules, because creating internal and external markers for affine variant nodules is an arduous task. Besides the aforementioned CADs, a multi-stage cancer detection method using watershed and multi-class SVM classifier was also proposed by [PERSON] et al. (2018) [12]. In their study, the median filter was used for the pre-processing of 500 lung CT images, and then thirteen different image features were extracted from the region of interest (ROI). In the first stage of classification, they try to detect any cancerous tumor nodules from the input CT exam. If there were cancerous tumors, they identified the clinical stages of those tumors in the second stage of classification. Otherwise, they calculated the probability of non-cancerous tumors becoming cancer in the third stage. For the first and second stages, they used the SVM classifier, and for the third stage, they calculated the area ratio (area of the nodule to the area of the entire lung). Their experiment reached accuracy levels of 97% for detection and 87% for staging, respectively. Our proposed CAD which applies two stages of classification for detection and staging is quite similar to that presented in [12]. However, we differ from them by extracting the locational features of cancerous tumors and using alternative machine learning algorithms for classification. Moreover, we determine the clinical stages of tumors using the eighth edition of the TNM staging method, which is a global, up-to-date standard for cancer staging. In their approach, they classified the stages (initial, middle, and final) roughly; thus, it was not very beneficial for precise treatment. Recently, deep learning models have made great breakthroughs for machine learning [13].