Automatic Detection And Diagnosis Of Pulmonary Nodules Using Deep Learning

Lung cancer has become one of the most threatening cancers to human physical and mental health and quality of life due to its high morbidity and mortality.If lung cancer can be screened and treated in time in the early stage,the mortality rate can be significantly reduced,and the 5-year survival rate of patients will be greatly improved.The large number of CT images,the lack of experienced radiologists and the long time of viewing images can easily lead to the aggravation of work burden and the decrease of work efficiency,as well as the serious misdiagnosis and missed diagnosis.At this stage,deep learning-based lung nodule detection and segmentation and lung nodule classification methods have the disadvantages of complex model design and low accuracy.Therefore,this article mainly focuses on the lung nodule detection and segmentation method based on attention feature fusion multi-task learning and the lung nodule classification method based on multi-scale convolutional neural network,and designs a lung nodule CT image based on the above method Auxiliary diagnosis and analysis system.The main research contents of this paper can be detailed as follows:(1)To solve the problems of low model accuracy and high computational complexity in the existing lung nodule detection and segmentation methods,an end-to-end lung nodule detection and segmentation approach based on attention feature fusion and multi-task learning is proposed.First of all,this method uses a multi-task learning model for modeling to achieve model feature information sharing and computational complexity reduction.Secondly,a multi-scale channel and spatial attention mechanism is proposed for channel and spatial attention and a residual attention feature fusion module is implemented,which can better realize the fusion of features with inconsistent semantics and scales.Finally,a new adaptive multi-task loss function can balance different types of tasks by balancing the weights of the main task and auxiliary tasks through constraints,avoiding the entire network from being dominated by simple tasks during the training process and leading to performance between tasks huge difference.The method is verified on the LIDC-IDRI dataset,and the results show that the approach proposed in this paper can effectively improve the performance of lung nodule detection and segmentation,achieves better results compared with the latest related approaches,and can achieve a balance between the performance of lung nodule detection and segmentation.(2)The current deep learning-based methods have achieved good results,but the manual design of complex network architecture is very resource-intensive and heavily relies on the knowledge and experience of researchers.Existing methods based on deep learning have many parameters,which cannot explain the meaning of each parameter and the operating mechanism of the model,and have poor interpretability.In response to the above problems,this paper proposes a lung nodule classification method based on neural architecture search.First,the neural architecture search technology is used to design an efficient 3D classification network structure,the attention residual cell is used as the basic unit of the search space and the partial order branch pruning method is used as the search strategy,which has reached a balance between network performance and search speed.Secondly,multi-scale channels and spatial attention modules are used in the network to improve the interpretability of feature description and category inference.Finally,the searched network architecture is fused with multiple models using the stacking method to obtain accurate prediction results of benign and malignant lung nodules classification.This paper conducts extensive experiments on the public data set LIDC-IDRI.Through comparison with the latest methods,it shows that the lung nodule classification method based on neural architecture search proposed in this chapter has better classification performance and faster convergence speed.(3)Design and implement a web-based CT lung nodule diagnosis system.The system consists of pulmonary CT image acquisition module,pulmonary nodules detection and segmentation module,pulmonary nodules classification module and intelligent analysis module.The doctor first logs in to the system and realizes the patient case data acquisition function through the image acquisition module.Then use the preset lung nodule detection and segmentation module and lung nodule classification model to automatically diagnose and return the diagnosis result to realize the automatic detection,segmentation and classification of lung nodules.The intelligent analysis module realizes the functions of assisting doctors in image reading and clinical diagnosis by displaying the automatic diagnosis results of lung nodules.The system realizes automatic screening and diagnosis of lung nodules in CT images,helping imaging doctors improve diagnosis efficiency and reduce workload.