Nodules detection by deep convolutional neural network and its application

Ruoxi Fang; Haotian Tang; Qiannuo Xu; Jinxiong You

doi:10.1117/12.2622996

1 December 2021 Nodules detection by deep convolutional neural network and its application

Ruoxi Fang, Haotian Tang, Qiannuo Xu, Jinxiong You

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792E (2021) https://doi.org/10.1117/12.2622996
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Pulmonary lung nodules, whether malignant or benign, often represent early symptoms of lung cancer. However, it is challenging to detect a pulmonary nodule by computed tomography before they become mass for helping people have timely treatment. In this paper, the idea we theorized is to scan the images to find their own pulmonary nodules, diagnose whether they are malignant or not, and report patients’ conditions. More specifically, we created a structure on top of the pre-build C3D structure. It achieves a deep convolution model with 6 layers and trains that by utilizing images from Lung Nodule Analysis 2016, which is the subset of Lung Image Database Consortium image collection (a diagnostic and lung cancer screening thoracic computed tomography scans datasets). These images are segmented into multiple pieces in the direction of the z-axis and pre-process with a more direct connection to the annotations from the big CT scans. After the diagnosis, it will be broadcasted in the form of long sentences and its voice implemented by Natural Language Processing (NLP). Experimental results illustrate that this CNN model has high robustness, which deserves confidence from people with potential pulmonary lung cancer.

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Ruoxi Fang, Haotian Tang, Qiannuo Xu, and Jinxiong You "Nodules detection by deep convolutional neural network and its application", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792E (1 December 2021); https://doi.org/10.1117/12.2622996

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KEYWORDS

Image segmentation

Lung

Lung cancer

Data modeling

3D modeling

Computed tomography

Human-machine interfaces

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