Small cell lung tumour differentiation using F-18 (Fluorine-18) PET and smoothing using Gaussian 3D convolution operator

The most common disease for smokers is lung cancer. The deadly type of lung cancer is Small Cell Lung Cancer (SCLC). Tumour identification becomes complicated these days. It is only in the final stage that this form of lung cancer can be detected. When the patient has some of the earlier symptoms of SCLC, they can be subjected to preliminary tests of cancer. So this paper presents the part of the identification of lung cancer by differentiating identified tumour cells using Fluorine-18 positron emission tomography (F-18 PET) and that can be smoothed using Gaussian 3D convolution operator. The performance analysis shows the lung image dataset showing differentiation of tumour cells by applying F-18 PET, smoothened image using Gaussian 3D convolution operator and simulation graphs for accuracy, sensitivity, precision which shows the improved accuracy and specificity using Gaussian 3D convolution operator.