Lung Cancer Detection using Spatial FCM with Levelset and Neural Network Classifier

Image processing

Lung cancer keeps on changing on various medical factors depending on topographic areas. The identification of Lung cancer at initial stages is of extreme importance if it is intended to degrade high mortality rate. More than 3/4th of the illness is identified with tobacco utilization. Furthermore, hereditary components, presentation to ecological poisons, second hand smoking expand illness quickly. Cures including chemotherapy, radiotherapy, surgery, epidermal open medications raise survival rate and personal satisfaction. This strategy is more about diagnosing at ahead of schedule and critical stages with keen computational procedures with different noise elimination by segmentation strategies and calculations which is the root idea of digital image processing. Location of CT pictures received from cancer research organizations is investigated utilizing MATLAB. In this paper images of lungs were taken for find various parameters of the texture. Mainly CT images of lungs can be categorised into normal and abnormal. Classification is based on the features extracted from the taken image. Implementation of the system focuses on texture based features e.g. GLCM (gray level co-occurrence matrix) feature plays an vital role in medical field.

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