Abstract
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
Highlights
Lung cancer is one of the deadliest forms of cancer worldwide and represents a significant threat to human health and life
The models for the detection and classification of lung nodules are mostly assessed according to sensitivity (SEN), specificity (SPEC), accuracy (ACC), precision (PPV), F1-score, receiver operating characteristic (ROC) curve, free-response operating characteristic (FROC), and area under the ROC curve (AUC), but the competition performance metric (CPM) can be used to assess their performance [27]
It has been noted that the leading solutions employed convolution neural network (CNN) and used the provided set of nodule candidates
Summary
Lung cancer is one of the deadliest forms of cancer worldwide and represents a significant threat to human health and life. Positive results in clinical studies have led to an upsurge in lung cancer detection using CAD models Adopting and using such systems can improve survival rates through the diagnosis of lung nodules at early stages. This work classifies the lung nodule segmentation literature based on different network architectures (general neural network and multiview CNN architecture), which will provide a clear understanding and intuition to a new researcher in the field for future research. For this purpose, the intended review study describes some recent and previous publications from reputable databases, including IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus, that have addressed the difficulties involved in diagnosing lung nodules.
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