Prof. Kuniaki Saito Profile

Prof. Kuniaki Saito

at Fujita Health Univ

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Proceedings Article | 1 June 2020 Paper

Automated classification method of lung tumor type using cytological image and clinical record

Ayumi Yamada, Atsushi Teramoto, Yuka Kiriyama, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, Masato Hoshi, Kuniaki Saito, Hiroshi Fujita

Proceedings Volume 11515, 1151505 (2020) https://doi.org/10.1117/12.2566957

KEYWORDS: Principal component analysis, Tumor growth modeling, Convolutional neural networks, Data modeling, Tumors, Feature extraction, Lung, Medical imaging, Image classification, Lung cancer

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Recently, as chemotherapy has advanced, it is important to accurately diagnosis the histological type (adenocarcinoma, squamous cell carcinoma and small cell carcinoma). In previous study, automated classification method for lung cancers in cytological images using a deep convolutional neural network (DCNN) was proposed. However, its classification accuracy is approximately 70%, therefore improvement in accuracy is required. In this study, we focus on liquid-based cytology images and clinical record. In this study, we aimed to improve the classification accuracy of lung cancer type by combining cytological images and electronic medical records. We aimed to develop of classification method of lung tumor type by combining cytological images and clinical record. First, the cytological images were collected. The original microscopic images were first cropped to obtain images with resolution 256 × 256 pixels. And then, we collected personal clinical data (age, gender, smoking status, laboratory test values, tumor markers and so on) corresponding to cytological images. Next, image features were extracted from cytological images using VGG-16 model pretrained on the ImageNet dataset. 4096 features before the fully connected layer were extracted. Then, these features were reduced dimensions by PCA. Image features obtained from the DCNN and clinical data corresponding to cytological images were given to the classifier. Finally, classification result of 3 histological categories was obtained. Evaluation results showed that classification by combining cytological images and clinical record improved classification accuracy than by cytological images alone. These results indicate that the proposed method may be useful for histological classification of lung tumor.

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