Computer Assisted Prognosis System for Automated Pulmonary Lump İdentification

To upgrade performance of Computer Assisted Prognosis(CAP) for automated pulmonary lump identification on Computer X-ray images which is Digital Imaging fields, this paper suggests an intelligent way for the development of a new support system. The major step in detection of any unusually in lung region is to obtain a Digital Tomography image. The digital format obtained of the images are really mobile, hence the extraction and distribution of specific information. A very large amount of connected images pose a challenge in consistency and consequently arriving at conclusion. The CAP system has been designed and developed to fragment the lung tumour area and draw out the features which is the region of interest. Lung segmentation and Feature extraction are two main steps for the Detection process. In segmentation of lung region, Fuzzy c mean along with Gray Level Cooccurence Matrix (GLCM) texture value, neural network is implemented. Finally, the properties which are mentioned above are used to divide lung tumour as genial or malignant. The main objective of this method is decrease false positive rate and to improve the access time and diminish inter-observer criteria.