Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy accurate way to detect by using image processing techniques on the cancer-affected images captured from patients. This paper proposes novel lung detection method. Firstly, adaptive median filter algorithm (AMF) applied preprocess those for improving quality of affected area. Then, supervised edge (SIED) presented segment images. feature extraction employed extract mean, standard deviation, energy, contrast, etc., Finally, unsupervised diffusion classification (UDC) explored narrow down areas. The proposed method implemented two datasets obtained hospital real-time values. experiment results achieved superior performance in cancer, which demonstrates that our new model can contribute early cancer.