Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session H71: Poster Session I (2:00pm - 4:00pm)
2:00 PM,
Tuesday, March 16, 2021
Abstract: H71.00120 : Auto segmentation, detection, and countering of Lung nodule using convolutional neural networks
Presenter:
Shahabeddin Mostafanazhad aslmarand
(Florida Atlantic University)
Authors:
Shahabeddin Mostafanazhad aslmarand
(Florida Atlantic University)
Touhid Feghhi
(Florida Atlantic University)
Dr. Wazir Muhammad
(Florida Atlantic University)
Lung cancer is one of the most common types of cancer and leading causes of cancer-related deaths in the United States. Whereas radiotherapy is often applied to treat lung cancer, delineation of the tumor area known as planning target volume (PTV) is a time-consuming and critical task. In this paper, we have proposed a method based on convolutional neural network for an auto detection of PTV to aid radiation treatment for lung cancer. Using a gradient descent search algorithm and convolutional neural network for classification, we can detect lung tumors and nodules. In doing so, pre-processing and segmentation are performed on lung CT images for obtaining the segmented tumor and non-tumor region. The trained algorithm results will be compared to pre-segmented and contoured CT images for validation and accuracy. The accuracy of our algorithm is 64 % in detecting lung nodules. We believe that the use of such algorithms will help in improving the clinical flow and efficiency of radiotherapy for lung cancer while reducing the chances of a missed diagnosis resulting in increase in the life expectancy of the patients.
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