Deep Convolutional Generative Adversarial Networks for Automated Segmentation and Detection of Lung Adenocarcinoma Using Red Deer Optimization Algorithm

Abstract

The diagnosis of early-stage lung cancer can be challenging due to several factors. Firstly, the asymptomatic nature of the disease means that it may not present any noticeable symptoms until it has progressed to later stages. Additionally, the use of computed tomography, which can be expensive and involves repetitive radiation exposure, can further complicate the diagnostic process. Even specialists may encounter difficulties when examining lung CT imagery to identify pulmonary nodules, particularly in the case of cell lung adenocarcinoma lesions. This paper suggests a unique deep learning-based Deep Convolutional Generative Adversarial Networks (DCGAN) model for lung cancer classification. The dataset utilized for the experimental purpose is accessed from the LUNA16 challenge database. This comprises 888 CT scans of the lungs. These images are initially segmented using Quick-CapsNet (QCN) model and applied with Red Deer Optimization (RDO) algorithm to extract the optimized features. Furthermore, the categorization between benign and malignant tumors is carried out using the DC-GAN model. The pulmonary nodule detection accuracy of the proposed model is 98.65%, indicating early-stage lung cancer. It is discovered to be superior to other existing techniques, such as sophisticated deep learning, straightforward machine learning, and hybrid methods applied to lung CT scans for nodule diagnosis. According to experimental findings, the suggested way can significantly help radiologists spot early lung cancer and facilitate prompt patient management.