Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification
Source abstract
Abstract
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 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 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
College of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan
Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan 701, Taiwan
Funding
the Ministry of Science and Technology of the Republic of China
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