Sensors, Vol. 19, Pages 3722: Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies

Sensors, Vol. 19, Pages 3722: Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies Sensors doi: 10.3390/s19173722 Authors: Nasrullah Nasrullah Jun Sang Mohammad S. Alam Muhammad Mateen Bin Cai Haibo Hu Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned feat...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research