Lung Cancer Detection Using Classification Algorithm

The most extreme and lethal illnesses on this world is mostly lung cancers. On the opposite hand, timely analysis and aid can keep lives. Detection of malignant lungs. Recently, imaged processing techniques were broadly used in many scientific fields to improve images in early detection and remedy. Various segmentation and magnification techniques were used to extract the normal and uncommon functions of the images. In this system Neuro-fuzzy classifier is used to detect cancer. For extracting features the image processing techniques applied are Pre-processing, Image Segmentation, filtering, Dilation and Image filling-to get more accurate findings. Once the lung CT image has been input, the pre-processing technique is used with the intention of improving the lung image data by suppressing unintentional distortions or enhancing lung image features crucial for future processing. The image has been segmented by thresholding after pre-processing. The threshold segmentation process can be regarded as the process of separated foreground background. By using a filtering process, the segmented image is enhanced or modified. Filtered images dilated using the dilation technique. Dilation adds images to the boundaries of objects in an image. The dilation and image filling stages will find the image is affected by cancer or not with neuro-fuzzy classification.