Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.

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Lung Nodule Detection: Image Enhancement using Fuzzy Rule Based Contrast Limited Adaptive Histogram Equalization and Entropy Weighted Residual Convolution Neural Network Method in CT

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d5328.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 7496-7502

Keyword(s):

Feature Extraction ◽

Image Enhancement ◽

Lung Nodule ◽

Fuzzy Rule ◽

Histogram Equalization ◽

Rule Based ◽

Lung Nodule Detection ◽

Adaptive Histogram Equalization ◽

Nodule Detection ◽

Entropy Weighted

Computed Tomography (CT) images are read by several lung nodule detection methods. The early step of contrast enhancement is mandatory because of low contrast in original image and further techniques of image processing are with unsatisfactory results. Hence this process are resulted an enhanced image of clearly discrete lung area from background. Image enhancement, feature extraction, and classification are three primary steps. In this work, Rule based Contrast Limited Adaptive Histogram Equalization (FRCLAHE) perform image enhancement step followed by feature extraction and Fuzzy Rule (FR) determines the contrast value. From rules upper contrast value are determined then image is enhancement from CLAHE. In the second, the feature extraction is conducted using the Fuzzy Continuous Wavelet Transform (FCWT) and Gray Level Feature Extraction (GLCM). After this step, the classification is completed using the Entropy Weighted Residual Convolution Neural Network (EWRCNN). Finally, the results are evaluated between the samples, compared to FP reduction with Faster R-CNN alone, the inclusion of rule‐ based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of the proposed EWRCNN approach to lung nodule detection and FP reduction on CT images.

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Content Based Image Retrieval Approach in Creating an Effective Feature Index for Lung Nodule Detection with the Inclusion of Expert Knowledge and Proven Pathology

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405610666140711125251 ◽

2014 ◽

Vol 10 (3) ◽

pp. 178-204 ◽

Cited By ~ 2

Author(s):

Preeti Aggarwal ◽

H.K. Sardana ◽

Renu Vig

Keyword(s):

Image Retrieval ◽

Expert Knowledge ◽

Lung Nodule ◽

Content Based Image Retrieval ◽

Lung Nodule Detection ◽

Nodule Detection

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Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191113122840 ◽

2020 ◽

Vol 16 (8) ◽

pp. 1004-1021

Author(s):

Xiaoqi Lu ◽

Yu Gu ◽

Lidong Yang ◽

Baohua Zhang ◽

Ying Zhao ◽

...

Keyword(s):

False Positive ◽

Contextual Information ◽

Lung Nodule ◽

Positive Sample ◽

Lung Nodules ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Level Information ◽

Multi Level ◽

Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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