An Efficient and Useful Hybrid Approach for Detection of Lung Cancer

Analysis of medical images is often intricate and time intense, even for experienced physicians. The aid of image scrutiny and machine learning can make this process easier.. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier recognition and treatment stages, in which the time issue is very vital to determine the infection in the patient as possible as fast, especially in various cancer tumors such as the lung cancer, breast cancer. Lung cancer images passed basic three stages to achieve more quality and accuracy in our experimental results, firstly image enhancement stage which is low pre-processing image techniques. Gabor filter, using a Gaussian rule in which produced the preeminent resultant enhanced images, In the image segmentation stage, thresholding segmentation mechanism by marker using the gradient magnitude as the segmentation function and computed the watershed renovate of the segmentation function. Finally features which assist to make a contrast between normal and abnormal images were. Two features computed were: black and white pixels percentage of the input image and the second feature is image Masking and tagging.