The overall survival rate for lung cancer patients is only 14%(five years). Early detection and treatment of lung cancers can improve the survival rate by 50% if the tumor is detected early at Stage 1, which is a solitary and circumscribed lung nodule. For the detection of lung cancer at an early stage, computed tomography (CT) is a more sensitive imaging modality. However, chest radiographs (CXRs) are used far more commonly for chest diseases because they are the most cost-effective, routinely available, and dose-effective diagnostic tool. Because CXRs are so widely used, improvements in the detection of lung nodules in CXRs could have a significant impact on early detection of lung cancer. Studies have shown that, however, 30% of nodules in CXRs were missed by radiologists in which nodules were visible in retrospect, and that 82–95% of the missed nodules were partly obscured by overlying bones such as ribs and clavicles. Such nodules would be more conspicuous on the soft-tissue images obtained by using the dual-energy subtraction technique. Therefore, a computer-aided detection (CADe) scheme for nodules in CXRs has been investigated for assisting radiologists in improving their sensitivity.
    Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important because radiologists are likely to miss such subtle nodules.  This design is to develop a CADe scheme using Matlab software with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, incorporating the VDE technology with CADe scheme is used. A nonlinear support vector classifier was employed for the classification of the nodule candidates. A publicly available database is used in the design.

Reference Paper: Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography
Author’s Name:
Sheng Chen and Kenji Suzuki
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