Deep learning-based computerized diagnosis of lung cancer

Abstract

The Deep-Learning (DL) technique is capturing increasingly flexible in the sector of processing medical images. Rapid and precise lung cancer detection requirements a standardized computer-aided diagnostic (CAD) architecture. For a quick and reliable detection of lung cancer, a standardized CAD framework is required. High-risk patients are advised by the National Lung Screening Trial to undertake standard screenings with low-dose CT to support the early detection of cancer and decrease the consequence of lung cancer death. In this paper, a lung CT scan and probabilistic bilateral convolutional neural networks (PB-CNN)-based automated diagnosis system for lung cancer are developed. The PB-CNN models were trained using sample cases from the LUNA16 dataset. We used existing techniques, such as Decision Trees (DT), Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) to detect lung cancer. We employed accuracy, precision, recall, and f-measure in our experimental investigation. The proposed PB-CNN is automatically detecting lung cancer, yielding an acceptable performance.