###### Abstract  
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer. Keywords:automatic diagnosis; backpropagation neural network; computed tomography; lung cancer; staging +
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region. Moreover, it is preferable for clinical routines that require an iterative analysis of the tumor during treatment [2]. Computed tomography (CT) is a standard imaging modality for lung cancer diagnosis. The National Lung Screening Trial (NLST) reported that the lung cancer mortality rate of 15-20% could be reduced by performing low-dose CT screening [3]. CTs display tumors on cross-sectional images of the body called \"slices\". These slices can be stacked together to reconstruct a three-dimensional structure which makes possible more comprehensive analysis of tumors. However, the manual interpretation of the CT scans is prohibitively expensive in terms of time and effort. Also, it is subject to the skill and clinical practices of the interpreter; hence, the diagnosis results may suffer from inter- and intra-observer variation [4]. Due to these inconveniences, any real-world application is necessary for automated interpretations of the CT scans. This fact has strongly motivated computer-aided diagnosis systems (CADs) to become an extensive research area in the field of biomedical engineering. Scientists have proposed a vast number of CADs for lung cancer diagnosis using modern image processing and machine learning techniques. Nonetheless, most of these CADs have focused only on the detection of pulmonary nodules. Nodules can evoke the possibility of lung cancer and appear as round or oval-shaped opacities on the CT scans [5]. Nonetheless, the detection of nodules does not provide sufficient information about the severity of the disease to make proper treatment decisions. Generally, half of the patients who undergo CT screening present more than one nodule, but not all of these nodules are cancerous [6]. Physicians usually use the terms \"nodule\" and \"tumor\" interchangeably when they are uncertain about the severity of the disease. Indeed, identifying the severity of a tumor nodule is a principal issue for lung cancer diagnosis. In clinical practice, the severity of lung cancer can be expressed as different stages using the Tumor, Node, and Metastasis (TNM) system [7]. As the name describes, this system assesses the severity of the disease based on three descriptors.