## 2 Related Works
The RGB color space was used to represent the color images. Two segmentation processes have been used, the first one was Fuzzy C-Mean Clustering algorithm (FCM), and the second one was the improved version of the Hopfield Neural Network for the classification of the sputum images into background, nuclei and cytoplasm. These two latter regions were used as a main feature to diagnose each extracted cell. It was found that the HNN segmentation results are more accurate and reliable than FCM clustering of all cases. The HNN succeeded in extracting the nuclei and cytoplasm regions. However, FCM failed in detecting the full nuclei. Partial detection occurred in most of the trials. In addition to that, the FCM is not sensitive to intensity variations as the segmentation error at convergence is larger with FCM compared to HNN. The proposed CAD system was tested by comparing its 100 case diagnosis to the diagnosis of an experimented pathologist of the same data, and the results showed that the CAD system and the pathologist agreed on 92% of the cases and 8% disagree, with a sensitivity of 92.5%, specificity of 69% and accuracy of 85%. While this CAD system was successful in detecting lung cancer cells, it has a number of limitations. For instance, the CAD system was associated with the high number of false positive rates, which make the chance of the patient's survival very low. Also, the constraint of fixing the numbers of clusters by the operators might compromise in some cases the quality of the segmentation. In [17] the authors described a method of sputum color image classification for detecting normal and abnormal cells by using computational intelligent techniques such as Tetrakis Carboxy Pheyl Porphine (TCPP). In this method the cancer cells have been labeled based on the increased number of low density. This algorithm takes into consideration the spatial information by using a region adjacency graph processing algorithm. Furthermore, they used 3D clustering and relaxation labeling approaches, where the segmentation results depend on the preprocessing step which determines the quality of the images. CT screening modalities have been also used for the detection and diagnosis of lung cancer. In [18] the authors presented a survey using computer analysis CT scan of the chest which can constantly detect the lung cancer in its early stages, however, it has a number of limitations with high false-positive rates, because it detects a lot of non-cancerous nodules and misses many small cancers nodules in addition to its low sensitivity for central lesions, plus it is invasiveness. In the literature, all the previous techniques have been used on medical images to detect diseases varying from tuberculosis to cancers. Methods based on sputum image analysis still suffer from a high number of false negatives, resulting in exposing a patient to unnecessary radiation and surgeries. In addition to that, most methods fail to consider the outlier pixels, which may sometimes represent a class in themselves, is resulting in cancer cells. Moreover, the preprocessing techniques need further enhancement to discard the debris cells in the background of the images and to remove all noise from the images, in addition to the overlapping between the sputum cells which are not considered by the previous techniques. Thus, segmentation results are not accurate enough to be used in the diagnosis part. In the HNN-based method, the cluster number has to be provided in advance. This affects the feature extraction part, especially in the presence of outliers. These problems have to be overcome, and more features have to be computed to develop a successful CAD system.