Design of Automatic Lung Nodule Detection System Based on Multi-Scene Deep Learning Framework

Nowadays, the efficient identification of the lung nodule greatly leads to the chance of lung cancer risk assessment. Hence, the exact locations of lung nodules are a critical and complicated task. Researchers in this area have been working widely for almost two years. However, previous computer-aided detection (CAD) modules, such as transforming CT, segmenting the lung nodule and extracting the features are mostly complex and time-consuming, because more modules will require the creation of a complete image processing system. In addition, certain state-of-the-art deep learning systems are specified in the database standard. For this purpose, this paper suggests an efficient identification system for lung nodules based on Multi-Scene Deep Learning Framework (MSDLF) by the vesselness filter. A four-channel CNN model is designed to enhance the radiologist's knowledge in the detection of four-stage nodules by integrating two image Scenes. This model can be applied in two different classes. The results show that the Multi-Scene Deep Learning Framework (MSDLF) is efficient for increasing the accuracy and significantly reducing false positives in an enormous amount of image data in the detection of lung nodules.