Abstract
Lung cancer is cancer that starts in the lungs. Cancer is a disease where cancerous cells grow out of control, taking over normal cells and organs in the body. The early detection of lung cancer is the most effective way to decrease the mortality rate. In this paper we compare two methods, a modified Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) Clustering Algorithm, used in segmenting sputum color images. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. Both methods are designed to classify the image of N pixels among M classes or regions. Due to intensity variations in the background of the raw images, a pre-segmentation process is developed to standardize the segmentation process. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM; however the latter was faster in converging.
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