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.
Highlights
Among different types of cancer, pulmonary cancer refer to as lung cancer is considered to be one of the most deadly cancers
The second phase combines the performance of independent detection with the classification results to provide the overall performance evaluation of the Computer-Assisted Diagnosis (CAD) system
We used FreeResponse Receiver Operating Characteristic (FROC) [30] including average sensitivity and the number of FPs per scan (FPs/scan) which is the official evaluation metric for LUNA16, where detection is considered a true positive if the location lies within the radius of a nodule centre
Summary
Among different types of cancer, pulmonary cancer refer to as lung cancer is considered to be one of the most deadly cancers. In 2018, there were approximately 2.2 million new pulmonary cancer cases and about 1.8 million deaths in U.S within a year. Pulmonary cancer is an uncontrollable abnormal lung cells growth, referred to as nodules, whose detection in early stages is highly crucial to the effective control of disease progression and potentially increase the survival rate of the patient. Used manual lung nodule delineation by radiologists on high-resolution and high-quality chest Computed Tomography (CT) is complex, time consuming and extremely tedious [1]. Automation of pulmonary nodule detection with effective and efficient Computer-Assisted Diagnosis (CAD) tools facilitates radiologists in fast diagnosis and improves the diagnostic confidence.
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