Deep Residual Learning for Lung Cancer Nodules Detection and Classification

Abstract

The incorporation of deep learning and image processing techniques has rendered the early identification of lung cancer critical and simple. There are an astounding five million deaths annually caused by lung cancer which makes it one of the leading killers of both sexes worldwide. In the case of lung illnesses, the data gleaned from a computed tomography (CT) scan might be quite helpful. The primary aims of this research are to (1) identify cancerous lung nodules in the input lung image and (2) rank the severity of the cancer present in each nodule. In order to detect lung cancer utilizing non-small cell lung cancer imaging, histological pictures of the lungs as well as CT scan data are acquired. The adenocarcinoma images, the big cell carcinoma images, the squamous cell carcinoma images, and the normal lung tissue images are the four subsets that are contained inside these two primary types of data. The quality of the obtained lung image can be improved by inspecting each individual pixel using the multilayer brightness-preserving technique, which also helps to remove unwanted background noise. Noise-reduced lung CT scans and lung cancer histopathological scans are used to separate the damaged area using an improved deep neural network with layer-based network segmentation.