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http://hdl.handle.net/10397/82207
| Title: | Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT | Authors: | Masood, A Yang, P Sheng, B Li, HT Li, P Qin, J Lanfranchi, V Kim, JM Feng, DD |
Issue Date: | 2019 | Source: | IEEE journal of translational engineering in health and medicine-JTEHM, 4 Dec. 2019, v. 8, p. 1-13 | Abstract: | Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4, 92, 96 and 98.51 with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People's Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7 sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients. | Keywords: | Cancer Lung Computed tomography Training Solid modeling Cloud computing Machine learning Computer-aided diagnosis Nodule detection Cloud computing Computed tomography Lung cancer |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE journal of translational engineering in health and medicine-JTEHM | EISSN: | 2168-2372 | DOI: | 10.1109/JTEHM.2019.2955458 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ The following publication A. Masood et al., "Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, pp. 1-13, 2020, Art no. 4300113, 1-13 is available at https://dx.doi.org/10.1109/JTEHM.2019.2955458 |
| Appears in Collections: | Journal/Magazine Article |
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