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Lung cancer is one of the most increasing diseases in the rapidly changing world. This disease can be cured in the initial stage. It should be identified at the early stage for the diagnosis purposes. The prediction of lung disease stages can be done using image processing techniques. The proposed algorithm consists of segmentation process using watershed segmentation and lung nodule detection using edge detection method. The segmented lungs are subjected towards the feature extraction process which includes the Gray Level Co-occurance (GLCM) algorithm. The main prediction can be done using the classifier process. The classifier which includes in the proposed work is the multilayer Perceptron neural network classifier. The classes of the tumor level can be predicted using the neural network. This prediction would indicate the early stage of the cancer and it will be helpful for the treatment of the cancerous unit in the human body. Once the tumor is detected, the tumor affected part can be identified using morphological operations which include various algorithms. A morphological operation consists of filling, dilation, open and close. This technique will give the total area affected and the region where the lung are affected. Thus, the achieved accuracy level of the proposed work is 90%.