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Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss

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Published:17 April 2020Publication History
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Abstract

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

References

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  1. Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss

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          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 1s
            Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging
            January 2020
            376 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/3388236
            Issue’s Table of Contents

            Copyright © 2020 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 17 April 2020
            • Accepted: 1 October 2019
            • Revised: 1 August 2019
            • Received: 1 June 2019
            Published in tomm Volume 16, Issue 1s

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