Nodule Detection In Chest Radiography Research Articles

Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.

The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist's diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model’s performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs.0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

CAD for Lung Nodule Detection in Chest Radiography using Complex Wavelet Transform and Shearlet Transform Features

Background and Objectives: The main objective of this paper is to extract the lung nodules in Chest Radiography (CR) for identifying and locating the presence of small lesion or nodules. Methods and Statistical Analysis: Four stages of works are carried out such as: 1). Image Registration using geometrical transformation method, 2). Lung Segmentation using thresholding method, 3). Feature Extraction using Complex Wavelet Transformation method and Shearlet transformation method and finally 4). Image Classification using Random Forest method and classify the images are normal or abnormal. To confirm the abnormality of the segmented output the results are compared with the labeled ground truth images given by medical experts. Findings: The entire proposed approach is implemented in MATLAB software and the performance is verified by comparing the results obtained from CWT results with the ST results. The proposed experiment is carried out on JSRT dataset and obtained the accuracy 96% which is better than the existing approach’s obtained accuracy is 95.4%. Conclusion and Improvements: The proposed approach in this paper is better than the existing approaches for lung nodule detection and identification through segmentation in chest radiography. In future work the performance of the proposed work is evaluated by comparing the obtained results with the other existing transformation methods.

A Computer Aided Pulmonary Nodule Detection System Using Multiple Massive Training SVMs

A computer aided pulmonary nodule detection system for chest radiography is proposed. The system consists of three models, viz., lung segmentation, lung nodule candidates detection and false positive reduction. Several innovations are offered in this system. The first one is that the detection of potential lung nodule candidates is conceived as a filtering process that searches for any region which has a spherical structure (where a potential nodule may happen to occur) in a chest radiograph by analysis eignvalues of Hessian matrix of an image. The second one is that a novel two stage classifier based method is developed to address the problem of false positive reduction in lung nodule detection, in which a rules based classifier is followed by a filter termed as multiple massive training supported vector machine (MTSVM), where the rules based classifier is employed to quickly remove obvious FP (outliers) so that their influence on the training of MTSVM was eliminated, the MTSVM is developed to further separate nodules from nonnodule candidates. Experimental results suggest that the proposed CAD scheme was superior to others in FPs reduction of lung nodule detection in chest radiography.

A New Computer Aided Detection System for Pulmonary Nodule Detection in Chest Radiography

A New Computer Aided Detection System for Pulmonary Nodule Detection in Chest Radiography

Observer training for computer-aided detection of pulmonary nodules in chest radiography

ObjectivesTo assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography.MethodsThe 140 CXRs (56 with a solitary CT-proven nodules and 84 negative controls) were divided into four subsets of 35; each were read in a different order by six readers. Lesion presence, location and diagnostic confidence were scored without and with CAD (IQQA-Chest, EDDA Technology) as second reader. Readers received individual feedback after each subset. Sensitivity, specificity and area under the receiver-operating characteristics curve (AUC) were calculated for readings with and without CAD with respect to change over time and impact of CAD.ResultsCAD stand-alone sensitivity was 59 % with 1.9 false-positives per image. Mean AUC slightly increased over time with and without CAD (0.78 vs. 0.84 with and 0.76 vs. 0.82 without CAD) but differences did not reach significance. The sensitivity increased (65 % vs. 70 % and 66 % vs. 70 %) and specificity decreased over time (79 % vs. 74 % and 80 % vs. 77 %) but no significant impact of CAD was found.ConclusionShort-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively.Key Points• Computer-aided detection (CAD) is increasingly used as an adjunct for many radiological techniques. • Short-term feedback does not improve reader performance with CAD in chest radiography. • Differentiation between true- and false-positive CAD for low conspicious possible lesions proves difficult. • CAD can potentially increase reader performance for nodule detection in chest radiography.