scholarly journals Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Mehedi Masud ◽  
Ghulam Muhammad ◽  
M. Shamim Hossain ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Data Analytics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Big Data Analytics ◽  
Pulmonary Nodules ◽  
Cognitive Computing ◽  
Deep Model ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

A Quantitative Method for Differentiating Malignant and Benign Pulmonary Nodules

2014 ◽  
Vol 513-517 ◽  
pp. 3830-3834
Author(s):  
Yu Zhao ◽  
Sheng Dong Nie ◽  
Jie Wu ◽  
Yuan Jun Wang
Keyword(s):  
Pulmonary Nodule ◽  
Quantitative Method ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Nodule Detection ◽  
Objective Standard ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Definition Of ◽  
Important Basis

It is common sense that CAD has great significance in the lung nodule detection. But it is still controversial whether the CAD can also automatically differentiates between malignant and benign pulmonary nodules. The primary cause of this controversy is due to the subjective definition of 9 characteristics of nodules which are important basis of nodule identification. In other word, these characteristics are too dependent on the doctor scoring, and no objective standard of them has built which make these characteristics can be obtained by calculation.The main aim of this paper is to establish a quantitative method of the characteristics and refine these nine characteristics. This new method is used to find the objective replacement (a series features which can be measured through algorithms) of these subjective characteristics of the pulmonary nodule detection with Bayesian analysis.The experiment of our method proves that it is feasible to substitute the features of Pulmonary Nodule obtained by calculating for the characteristics of the nodule which only used to be gotten by the subjective judgment of doctors.

scholarly journals 3D multi-scale deep convolutional neural networks for pulmonary nodule detection

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244406
Author(s):  
Haixin Peng ◽  
Huacong Sun ◽  
Yanfei Guo
Keyword(s):  
Neural Networks ◽  
Pulmonary Nodule ◽  
False Positive ◽  
Rapid Development ◽  
Pulmonary Nodules ◽  
Multi Scale ◽  
False Positive Reduction ◽  
Prediction Probability ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has achieved some successes. However, the sizes of pulmonary nodules vary greatly, and the pulmonary nodules have visual similarity with structures such as blood vessels and shadows around pulmonary nodules, which make the quick and accurate detection of pulmonary nodules in CT image still a challenging task. In this paper, we propose two kinds of 3D multi-scale deep convolution neural networks for nodule candidate detection and false positive reduction respectively. Among them, the nodule candidate detection network consists of two parts: 1) the backbone network part Res2SENet, which is used to extract multi-scale feature information of pulmonary nodules, it is composed of the multi-scale Res2Net modules of multiple available receptive fields at a granular level and the squeeze-and-excitation units; 2) the detection part, which uses a region proposal network structure to determine region candidates, and introduces context enhancement module and spatial attention module to improve detection performance. The false positive reduction network, also composed of the multi-scale Res2Net modules and the squeeze-and-excitation units, can further classify the nodule candidates generated by the nodule candidate detection network and screen out the ground truth positive nodules. Finally, the prediction probability generated by the nodule candidate detection network is weighted average with the prediction probability generated by the false positive reduction network to obtain the final results. The experimental results on the publicly available LUNA16 dataset showed that the proposed method has a superior ability to detect pulmonary nodules in CT images.

scholarly journals DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

2018 ◽  
Author(s):  
Wentao Zhu ◽  
Yeeleng S. Vang ◽  
Yufang Huang ◽  
Xiaohui Xie
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodule ◽  
Medical Image ◽  
Partial Information ◽  
Ct Images ◽  
Neural Nets ◽  
Training Dataset ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Weakly Supervised

AbstractRecently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms.1

Big Data Analytics for Medical Image Analysis in Screening and Detection of Pancreatic Tumor

2019 ◽  
Author(s):  
K. Sujatha ◽  
R. Shobarani ◽  
J. Veerendra Kumar ◽  
V. Karthikeyan ◽  
Sai Krishna ◽  
...  
Keyword(s):  
Image Analysis ◽  
Big Data ◽  
Data Analytics ◽  
Medical Image ◽  
Pancreatic Tumor ◽  
Medical Image Analysis ◽  
Big Data Analytics

scholarly journals Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liwei Liu ◽  
Xin Wang ◽  
Yang Li ◽  
Liping Wang ◽  
Jianghui Dong
Keyword(s):  
Pulmonary Nodule ◽  
Detection Method ◽  
Hessian Matrix ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Two Dimensional ◽  
Circular Area ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Pulmonary Ct

A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.

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