Abstract
Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.
Highlights
Lung cancer is considered as the deadliest cancer worldwide
We present a review of recent deep learning techniques for lung cancer detection
We presented different deep computer-aided diagnosis (CAD) systems and models that pursue the common objective of alleviating the work of radiologists in lung nodule detection
Summary
Lung cancer is considered as the deadliest cancer worldwide. For this reason, many countries are developing strategies for the early diagnosis of lung cancer. The NLST trial [1], showed that three annual screening rounds of high-risk subjects using low-dose Computed Tomography (CT). Reduce the death rates considerably [2]. These measures mean that an overwhelming quantity of CT scan images will have to be inspected by a radiologist. Since nodules are very difficult to detect, even for experienced doctors, the burden on radiologists increases heavily with the number of CT scans to analyze. With the expected increase in the number of preventive/early-detection measures, scientists are working in computerized solutions that help alleviate the work of doctors, improve diagnostics’
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