TY - GEN
T1 - 3d-cnn based computer-aided diagnosis (cadx) for lung nodule diagnosis
AU - Tai, Tzu Chi
AU - Tian, Miao
AU - Cho, Wei Ting
AU - Lai, Chin Feng
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - For the lung nodule screening, one of the commonly testing methods is the chest radiograph. However, it is difficult to judge with the naked eye with the initial nodule size is usually less than one centimeter. It is known that skilled pulmonary radiologists have a high degree of accuracy in diagnosis, but there remain problems in disease diagnosis. These problems include the miss rate for diagnosis of small nodules and the diagnosis of change in preexisting interstitial lung disease. The recent studies have found that 68% lung cancer nodules in radiographs can be detected by one reader and 82% by two readers. In order to solve this problem, we proposed a 3D-CNN predicting model to differ malignant nodules from all nodules in computed tomography scan. In the experiment results, the model was able to achieve a training accuracy of 100% and a testing accuracy of 94.52%. It shows the proposed model is able to be used for improving the accuracy of detecting nodules.
AB - For the lung nodule screening, one of the commonly testing methods is the chest radiograph. However, it is difficult to judge with the naked eye with the initial nodule size is usually less than one centimeter. It is known that skilled pulmonary radiologists have a high degree of accuracy in diagnosis, but there remain problems in disease diagnosis. These problems include the miss rate for diagnosis of small nodules and the diagnosis of change in preexisting interstitial lung disease. The recent studies have found that 68% lung cancer nodules in radiographs can be detected by one reader and 82% by two readers. In order to solve this problem, we proposed a 3D-CNN predicting model to differ malignant nodules from all nodules in computed tomography scan. In the experiment results, the model was able to achieve a training accuracy of 100% and a testing accuracy of 94.52%. It shows the proposed model is able to be used for improving the accuracy of detecting nodules.
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U2 - 10.1007/978-981-15-6113-9_5
DO - 10.1007/978-981-15-6113-9_5
M3 - Conference contribution
AN - SCOPUS:85087274456
SN - 9789811561122
T3 - Communications in Computer and Information Science
SP - 35
EP - 43
BT - Cognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
A2 - Shen, Jian
A2 - Chang, Yao-Chung
A2 - Su, Yu-Sheng
A2 - Ogata, Hiroaki
PB - Springer
T2 - 2nd International Cognitive Cities Conference, IC3 2019
Y2 - 3 September 2019 through 6 September 2019
ER -