## 1 Introduction  
Lung cancer ranks as one of the most common causes of death amongst all diseases. While there have been a lot of approaches to minimize the fatalities caused by this disease, early detection is considered the best step towards effective treatment. The overall five-year survival rate for lung cancer is 14%. Nonetheless, patients at the early stage of the disease who undergo curative resection have a five-year survival rate of 40% to 70%. The most recent estimate statistics, according to the American Cancer Society, indicate that in 2014, there were 224,210 new cases, accounting for about 13% of all cancer diagnoses. Lung cancer accounts for more deaths than any other cancer in both men and women. An estimated 221,200 new cases of lung cancer are expected in 2015. Furthermore, an estimated 158,040 deaths are expected to occur in 2015, accounting for about 27% of all cancer deaths [1]. The detection of lung cancer can be achieved in several ways, such as computed tomography (CT), magnetic resonance imaging (MRI), and X-ray. All these methods consume a lot of resources in terms of both time and money, in addition to their invasiveness. Recently, scientists have proven that the non-invasive technique of sputum cell analysis can assist in the successful diagnosis of lung cancer. A computer-aided-diagnosis (CAD) system using this modality would be of great support for pathologists when dealing with large amounts of data, in addition to relieving doctors from tedious and routine tasks. The design and development of sputum color image segmentation is an extremely challenging task. A part from the work reported in [2], where the authors used Hopfield Neural Network (HNN) to classify the sputum cells into cancer or non-cancer cell, little or nothing has been done in developing a CAD system based on sputum cytology. In this paper a state-of-the-art CAD system is implemented based on sputum color image analysis. The CAD system can play a significant role in early lung cancer detection. It serves as a useful second opinion when physicians examine patients during lung cancer screening [3]. A CAD system involves a combination of image processing and artificial intelligent techniques that can be used to detect abnormalities in medical images as well as enhancing medical interpretation for a better performance in the diagnosis process. In addition, a CAD system could direct the pathologist's attention to the regions where the probability of presence of the disease is greater [4]. On the other hand, the major role of the CAD system is to improve the sensitivity of the diagnosis process and not to make decisions about the patient's health status [5]. The proposed CAD system was tested on 100 sputum color images for early lung cancer detection, the experimental results were substantially improved, with high values of sensitivity, specificity and accuracy, in addition to an accurate detection of the cancerous cells when compared with the pathologist's diagnosis results. Therefore, the new CAD system could increase the efficiency of the mass screening process by detecting the lung cancer candidates successfully and improve the performance of pathology in the diagnosis process. The novelty of this work is defined as follows: a state-of-the-art complete CAD system is implemented based on the sputum color image analysis, and optimal deployment and combination of existing image processing and analysis techniques for building the computer aided diagnosis (CAD) system is used. The contributions can be summarized as follows: (1) Detection of sputum cell using a Bayesian classification framework; (2) Best color space after analysis of the images with histogram analysis; (3) Mean shift technique for the sputum cell segmentation; (4) Feature extraction, where a set of features are extracted from the nucleus region to be used in the diagnosis process.