Ms. Xuejuan Wang Profile

Xuejuan Wang

at Yunnan Normal University

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Proceedings Article | 23 July 2022 Paper

Classification of lung cancer complicated with pulmonary embolism based on x-ray image with transfer learning

Xuejuan Wang, Yonghang Tai

Proceedings Volume 12277, 122770Z (2022) https://doi.org/10.1117/12.2616487

KEYWORDS: Lung cancer, X-rays, X-ray imaging, Image classification, Data modeling, Lung, Artificial intelligence, Cancer, Tumor growth modeling, Image processing

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The probability of pulmonary embolism in lung cancer is more than 20 times that of normal people, and its mortality increases by 2 to 8 times. Once pulmonary embolism occurs in lung cancer patients, it will increase the difficulty of diagnosis and treatment and shorten the survival period. Therefore, early diagnosis and prevention are critical. Imaging is a non-invasive technique used to identify individuals with symptoms of disease. Advantages of plain X-Ray photography: easy to use, economical and inexpensive. However, the use of X-Ray for the diagnosis of lung cancer in combination with pulmonary embolism often requires a very experienced imaging physician. This limits the large-scale diagnosis of the population. In recent years, it turns out that more and more research is based on the application of deep learning to images. And medical imaging is one of the most promising directions, with systems based on deep learning and image processing providing aids to decision-making in the diagnosis and prevention of many diseases. This paper compares several methods three layer neural network, EficientNetB0, VGG16 , KNN, SVM and transfer learning for automatic multi-classification of lung cancer images. We are using a available dataset from Yunnan First People's Hospital with X-Ray images from normal (160), lung cancer (1218) and lung cancer complicated with pulmonary embolism (88). As you can see, our dataset is not perfect and the imbalance between the three categories is more prominent. Therefore, we tried transfer learning using fine-tuning, which resulted in an accuracy rate of over 95%, and is proved to be the best performance. The method can assist doctors in decision making and improve diagnostic efficiency.

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