A Novel Integrated Prognosis & Diagnosis System for Lung Cancer Disease Detection using Soft Computing Techniques

Abstract

 Nowadays, lung cancer is one of the ranking first causes of mortality worldwide among men and women. Although there are a lot of treatment options like surgery, radiotherapy, and chemotherapy, five-year survival rate for patients is quite low. However, survival rate may go up to 54% in case lung cancer is identified in an early stage. Therefore, early detection of lung cancer is vital to decrease lung cancer mortality. Medical Experts are continuously trying to find the best solution for the early prediction and diagnosis of Lung Cancer Disease; in this Research work, an attempt has been made to design and develop a novel integrated soft computing predictive system to handle various types of patients’ clinical data to diagnose the lung cancer disease. Here data mining techniques are used to handle the numeric and textual data, image processing techniques are used to handle CT scan images, neural networks are used to train the lung cancer patient images, and fuzzy inference mechanism is used to predict the lung cancer stages. This integrated approach results in detection of lung cancer disease with Prognosis and suggesting diagnosis by the expert system for lung cancer disease. Even in cases of small-sized nodules (3–10 mm), the proposed system is able to determine the nodule type with 96% accuracy.