Lung Cancer Detection Through Filtration and Segmentation via Neural-Network

Cancer is a threat in which the cell starts to grow abnormally and it does not function like a normal cell. When the cancerous cell begin to develop in lungs it is called as lung cancer. Lung cancer is one of the most significantly diagnosed form of cancer.It is getting hard for many doctors to state the conclusion after looking at the Computed Tomography(CT) image. So, there is a need to detect lung cancer at an early stage possible. An improvised version of VGG-19 Model is proposed for detection of lung cancer in this paper. At first filtration an d segmentation of lesion from CT scan images of lungs is performed. Median and Gaussian filters a re included i n t he fi ltration me thod whereas, segmentation work involves the Gaussian mixture model, Fuzzy-C-Means and proposed mean-based segmentation. The proposed work is evaluated on the publically available Data Bowl 2017 database and compared with other state-of-the-art approaches. It is observed that in the filtration part, Gaussian filter provides the highest accuracy of 99.02% where as mean-based segmentation outperforms other methods with an accuracy of 94.13%.