A new approach for lung cancer diagnosis: parallel feature learning

Abstract

Lung cancer, one of the most common types of cancer worldwide, can be fatal. Early diagnosis saves lives. Computed tomography (CT) is used in the diagnosis of the disease. Since the radiology specialist evaluates this X-ray result, the specialist's interpretation can vary. Furthermore, the analysis by the radiologist is both time-consuming and costly. However, a cancer diagnosis approach based on deep learning models supports the radiologist's decision. In this study, a parallel feature learning architecture developed for lung CT images was designed. This architecture focuses on learning different features from each parallel path by using deformable and dilated convolution layers together. Dilated convolution captures semantic features in images with different dilated rates ratios by expanding receptive field, while deformable convolution better captures structural changes. This mechanism allows for more flexible and distinctive feature extraction without significantly increasing computational complexity. The proposed architecture was tested on three different lung cancer datasets: the Public Lung Cancer Dataset, IQ-OTH/NCCD, and LIDC-IDRI. Experimental findings demonstrate robust and consistent classification performance, achieving accuracy rates of 99.44%, 98.75%, and 98.62%, respectively. This shows that the proposed architecture offers a reliable solution for lung cancer diagnosis.