Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification

Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification

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Cancer is the leading cause of death worldwide.Lung cancer, especially, caused the most death in 2018 according to the World Health Organization.Early diagnosis and treatment can considerably reduce mortality.

To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is Pool Chemicals increasingly being used in computer-aided design systems.However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time.In the NEW Neff V6320X1GB 7kg Wash 4kg Dry 1400rpm Integrated Washer Dryer -White present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data.

Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time.The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.