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Lung Cancer Diagnosis from Computer Tomography Images Using Region Based Segmentation and Fuzzy Logic

Lungs are the essential organs for respiration (inspiration and expiration) situated at thoracic cavity. Today, the lung cancer is serious disease in the world causing large number of deaths. The cells of all living organisms normally divide and grow in a control manner. When this control
 process is lost and tissues start expands then the situation is called cancer. Among the various cancers like bone cancer, breast cancer, blood cancer etc., the lung cancer is the most deadly one. The most preferred option for treating the lung cancer in the final stage is surgical removal
 of the diseased lung. Hence it is necessary to detect the lung cancer at an early stage to limit the danger. In this project the lung cancer diagnosis system based on Fuzzy Inference System (FIS) is proposed to detect the lung cancer at the early stage. The FIS plays a vital role in the medical
 field to provide medical assistance to the radiologist to diagnose the abnormality in the medical images. The proposed system first segments the suspected lung nodules from the input CT lung image using region based segmentation and classifies the suspected nodules as either benign (normal)
 or Malignant (cancerous) based on the feature extraction. Then the extracted features are given to the input of FIS. The Fuzzy system finds the severity of the suspected lung nodules based on IF-THEN rule.