Research On CT Image Lung Nodule Detection And Classification Algorithm Based On Deep Learning

Lung cancer is currently the cancer with the highest incidence and mortality rate worldwide.Lung nodules are the most important and common manifestations of early lung cancer,and accurate detection of lung nodules plays a very critical role in the treatment of early lung cancer.Currently,doctors mainly rely on CT scan technology to diagnose lung nodules,but due to the large number of lung CTs and the small size of lung nodules in the early stage of lung cancer,doctors rely only on the naked eye to analyze and diagnose,which requires great efforts and is very prone to miss or misdiagnosis.To address the current problems,this paper investigates the lung nodule detection and classification algorithm based on 3D convolutional neural network,and the main research contents are as follows.(1)Aiming at the difficulty of extracting pulmonary nodule features,this paper proposes a Faster R-CNN algorithm for the detection of candidate nodules by fusing residual attention networks.The attention mechanism can concentrate attention on the target nodule region by suppressing the unrelated features of the image,improve the feature extraction ability,and the residual network can effectively adapt to the network of various depths,avoiding the phenomenon of gradient degradation caused by the increase of network depth.By combining the residual attention network with Faster R-CNN,the detection ability of pulmonary nodules is effectively improved.Experimental results show that the sensitivity of the algorithm reaches 96.5%and the accuracy rate reaches 94.3%.(2)In order to further reduce the number of false positives in candidate nodules,this paper proposes a GC-ResNet based 3D CNN algorithm.The algorithm integrates GCNet based on residual module,obtains more feature information by modeling the global context,improves the detection and classification performance of small size nodules,and achieves the purpose of reducing false positives.In addition,the Dr Loss function is introduced to solve the problem of unbalanced training data categories.Experimental results show that the CPM value of the model reaches 90.2%,which verifies the effectiveness of the algorithm in the classification task.(3)Combining the lung nodule detection and classification algorithm proposed in this paper,an auxiliary lung cancer detection system with B/S architecture is built.The system realizes the functions of patient information entry,patient case view,and automatic detection of lung nodules.Finally,functional and performance tests are conducted on the system.The test results show that the functions and performance of the system basically meet the design requirements,and it has certain application value for clinical lung nodule detection.