“Classification and Detection of Lung Cancer Nodule using Deep Learning of CT Scan Images”: A Systematic Review

Abstract

Abstract Lung cancer is considered as the common cancerous neoplasms across the globe. In 2018, the World Health Organization (WHO) statistics approximated 2.09 million lung cancer cases with 1.76 million deaths globally. Early identification is an important aspect of providing the greatest chance of healing the patients. The objective of this manuscript was to explore how Deep Learning (DL) performs when the method is evaluated on datasets that are not from LUNA 16 for detection of pulmonary nodule and categorization of computed tomography scans. This report covered only peer-reviewed, original research papers using DL technology, and only findings were included from testing on datasets other than LUNA-16 and LIDC-IDRI. Deep learning utilizes Computed-Tomography (CT) to automatically improve the precision of an initial diagnosis of lung cancer. Consequently, this manuscript presents a short yet important review of DL methods to solve the extraordinary challenges of detecting lung cancer. In addition, this paper also traces the various causes, types, and treatment procedures of lung cancer. The fundamental principles of deep learning and CT have been described. A review of the various lung cancer detection methods via deep learning has been presented. Finally, discussions have been provided for further improvisation of the deep learning method. 9 studies investigated pulmonary nodule detection performance, 10 studies investigated the classification of pulmonary nodule performance, and 16 studies documented of pulmonary nodule for both classification and detection. Some of prominent DL methods which have been successful in detection and categorization of lung cancer nodules are Computer Aided Detection (CAD), Wavelet Recurrent Neural Network (WRNN), Optimal Deep Neural Network (ODNN), Massive Artificial Neural Network (MTANN) and Convolutional Neural Network (CNN) Training. Among, these DL methods, in most cases CNN achieved higher accurate results. The reports CNN achieved results between 73%-96.73% for both classification and detection. The CNN achieved results between 76%-99.2% for lung nodules classification and also achieved the results between 74.6%-97.78% for lung nodule detection. In addition to this, it was found that other DL method i.e., MTANN achieved the accurate results between 97%-100% for detection which came out to be superior related to other DL approaches.