Affiliations: [a] University of Tunis el Manar, Higher Institute of Medical Technologies of Tunis, Research Laboratory of Biophysics and Medical Technologies, 1006 Tunis, Tunisia
| [b] University of Tunis el Manar, Faculty of Medicine of Tunis, 1007 Tunis, Tunisia
| [c] Department of Medical Imaging, Abderrahmen Mami Hospital, 2035 Ariana, Tunisia
Correspondence:
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Corresponding author: Rekka Mastouri, University of Tunis el Manar, Higher Institute of Medical Technologies of Tunis, 1006 Tunis, Tunisia. E-mail: [email protected].
Abstract: BACKGROUND:Lung cancer is the most common cancer in the world. Computed tomography (CT) is the standard medical imaging modality for early lung nodule detection and diagnosis that improves patient’s survival rate. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have become a preferred methodology for developing computer-aided detection and diagnosis (CAD) schemes of lung CT images. OBJECTIVE:Several CNN-based research projects have been initiated to design robust and efficient CAD schemes for the detection and classification of lung nodules. This paper reviews the recent works in this area and gives an insight into technical progress. METHODS:First, a brief overview of CNN models and their basic structures is presented in this investigation. Then, we provide an analytic comparison of the existing approaches to discover recent trend and upcoming challenges. We also introduce an objective description of both handcrafted and deep learning features, as well as the types of nodules, the medical imaging modalities, the widely used databases, and related works in the last three years. The articles presented in this work were selected from various databases. About 57% of reviewed articles published in the last year. RESULTS:Our analysis reveals that several methods achieved promising performance with high sensitivity rates ranging from 66% to 100% under the false-positive rates ranging from 1 to 15 per CT scan. It can be noted that CNN models have contributed to the accurate detection and early diagnosis of lung nodules. CONCLUSIONS:From the critical discussion and an outline for prospective directions, this survey provide researchers valuable information to master the deep learning concepts and to deepen their knowledge of the trend and latest techniques in developing CAD schemes of lung CT images.
Keywords: CAD of CT images, deep learning, lung cancer screening, lung nodule detection, lung nodule classification
