Proposal on Detection of Lung Cancer on Reduced Images Using Foggy K-Means Clustering Algorithm
Keywords:
Dimensionality reduction, clustering, segmentation, PPA algorithm, histogram equalization, neural networks, Computed Tomography, Foggy k-means clustering.Abstract
Lung cancer is the second most common cancer in both women and men and is by far the most pivotal cause of cancer death among both men and women. Each year, a huge count of people die of lung cancer than of colon, breast, and prostate cancers combined. Early detection of lung cancer can increase the chance of survival among people. The overall 5-year survival rate for lung cancer patients increases from a meagre 14% to a decent 49% if the disease is detected at the right time. Although Computed Tomography (CT) can be more efficient than X-ray, however, problem was the time constraint in detecting the presence of lung cancer cells using the several diagnosis methods used. Hence, a lung cancer detection system using image processing is used to classify the present of lung cancer in a CT- images. In this project, MATLAB has been used through every procedure made. This involves image pre-processing, segmentation based on PPA, Foggy K-Means clustering and feature extraction by Neural network. The aim is to get more accurate results by using various enhancement and segmentation techniques.
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