scholarly journals Classification of Pulmonary Nodules by Using Hybrid Features

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmet Tartar ◽  
Niyazi Kilic ◽  
Aydin Akan
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Classification Accuracy ◽  
Pulmonary Nodules ◽  
Detection Performance ◽  
Diagnosis And Treatment ◽  
New Classification ◽  
Hybrid Features ◽  
Classification Approach

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

scholarly journals Pulmonary Nodule Classification with 3D Convolutional Neural Networks

2019 ◽  
Author(s):  
Anthony E. A. Jatobá ◽  
Lucas L. Lima ◽  
Marcelo C. Oliveira
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Neural Networks ◽  
Early Detection ◽  
Patient Survival ◽  
Pulmonary Nodules ◽  
Nodule Volume ◽  
3D Cnn ◽  
Nodule Classification

Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classification of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a sensibility of 84.83%. Moreover, our results have shown that the first slices of a nodule provide the best results and only five nodule slices are enough for a 3D CNN achieve its best results.

scholarly journals Integrated System for Classification of Pulmonary Nodules on CT Images

2019 ◽  
Vol 8 (4) ◽  
pp. 10893-10901
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Probabilistic Neural Network ◽  
Research Work ◽  
Pulmonary Nodules ◽  
Integrated System ◽  
Lung Nodules ◽  
Dimension Vector ◽  
Hybrid Features

Mortality rate of lung cancer is increasing very day all over the world. Early stage lung nodules detection and proper treatment is solution to reduce the deaths due to lung cancer. In this research work proposed integrated CADe/CADx system segments and classifies lung nodules into benign or malignant. CADe phase segments Well Circumscribed Nodules (WCN), Juxta Vascular Nodules (JVN) and Juxta Pleural Nodules (JPN) of different size in diameter. This part uses algorithms proposed in our previous WCN, JVN and JPN lung nodules segmentation work. CADx performance classification of segmented WCNs, JVNs and JPNs nodules into benign or malignant. In first part of CADx system hybrid features of segmented lung nodules are extracted and features dimension vector is reduced with Linear Discrimination Analysis. Finally, Probabilistic Neural Network uses reduced hybrid features of segmented nodules to classify segmented nodules as benign or malignant. Proposed integrated system achieved high classification accuracy of 94.85 for WCNs, 97.65 for JVNs and 97.96 for JPNs of different size in diameter (nodules diameter< 10mm, nodules diameter >10mm and < 30mm, nodules diameter >30mm and <70mm). For small nodules achieved classification performance values are, accuracy of 94.85, sensitivity of 90 and specificity of 95.85. And nodules of size 10mm to 30mm obtained accuracy, sensitivity and specificity are 97.85, 97.65 and 94.15 respectively.

Enhanced Backpropagation Approach for Identifying Genetic Disease

2014 ◽  
Vol 622 ◽  
pp. 75-80
Author(s):  
Baskar Nisha ◽  
B. Madasamy ◽  
J.Jebamalar Tamilselvi
Keyword(s):  
Machine Learning ◽  
Genetic Disease ◽  
Classification Accuracy ◽  
Gene Selection ◽  
New Classification ◽  
Backpropagation Algorithm ◽  
High Classification Accuracy ◽  
Disease Associations ◽  
Disease Analysis

Classification of data on genetic disease is a useful application in microarray analysis. The genetic disease data analysis has the potential for discovering the diseased genes which may be the signature of certain diseases. Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data. Among numerous existing methods for gene selection, Backpropagation algorithm has become one of the leading methods and it gives less classification accuracy. It aims to develop a new classification algorithm (Enhanced Backpropagation Algorithm) for genetic disease analysis. Knowledge derived by the Enhanced Backpropagation Algorithm has high classification accuracy with the ability to identify the most significant genes.

scholarly journals Proposed new classification of bronchoscopic findings in lung cancer cases.

Haigan ◽  
1986 ◽  
Vol 26 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Kenkichi Oho ◽  
Ryuta Amemiya ◽  
Masahiro Kaneko ◽  
Tetsuro Kodama ◽  
Masahiro Fukuoka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
New Classification

scholarly journals Generative augmentation to improve lung nodules detection in resource-limited settings

2020 ◽  
pp. 60-69
Author(s):  
Nataliya Gusarova ◽  
Artem Lobantsev ◽  
Aleksandra Vatian ◽  
Anton Klochrov ◽  
Maxim Kabyshev ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Resource Constraints ◽  
Pulmonary Nodules ◽  
Lung Nodules ◽  
Resource Limited ◽  
Initial Dataset ◽  
Unbalanced Dataset ◽  
Expert Assessments ◽  
Computational Resources

Introduction: Lung cancer is one of the most formidable cancers. The use of neural networks technologies in its diagnostics is promising, but the datasets collected from real clinical practice cannot cover a variety of lung cancer manifestations.  Purpose: Assessment of the possibility of improving the classification of pulmonary nodules by means of generative augmentation of available datasets under resource constraints. Methods: We used part of LIDC-IDRI dataset,  the StyleGAN architecture for generating artificial lung nodules and the VGG11 model as a classifier. We generated pulmonary nodules using the proposed pipeline and invited four  experts to visually evaluate them. We formed four experimental datasets with different types of augmentation, including use of synthesized data, and we compared the effectiveness of the classification performed by the VGG11 network when training for each dataset. Results: 10 generated nodules in each group of characteristics were presented for assessment. In all cases, positive expert assessments were obtained with a Fleiss's kappa coefficient k = 0.6–0.9. We got the best values of ROCAUC=0.9604 and PRAUC=0.9625 with the proposed approach of a generative augmentation. Discussion: The obtained efficience metrics are superior to the baseline  results obtained using comparably small training datasets, and slightly less than the best results achieved using much more powerful computational resources. So, we have shown that one can effectively use for augmenting an unbalanced dataset a combination of StyleGAN and VGG11, which does not require large computing resources as well as a large initial dataset for training.

scholarly journals Lung Cancer Detection using CT SCAN Images

2021 ◽  
pp. 611-633
Author(s):  
Dr. M. V. Karthikeyan ◽  
Aravindh. S ◽  
P. Laxman Guru Vignesh
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Imaging Modality ◽  
Lung Nodule ◽  
Computational Cost ◽  
Pulmonary Nodules ◽  
Detection Performance ◽  
Lung Segmentation ◽  
Wide Range ◽  
Lung Cancer Detection

Detection of pulmonary nodules has a crucial effect on the diagnosis of lung cancer, but the detection is a nontrivial task, not only because the appearance of pulmonary nodules varies in a wide range, but also because nodule densities have low contrast against adjacent vessel segments and other lung tissues. Computed tomography (CT) has been shown as the most popular imaging modality for nodule detection, because it has the ability to provide reliable image textures for the detection of small nodules. The development of lung nodule CADe systems using CT imaging modality has made good progress over the past decade. Generally, such CADe systems consist of three stages: 1) image pre-processing, 2) initial nodule candidates (INCs) identification, and 3) false positive (FP) reduction of the INCs with preservation of the true positives (TPs). In the pre-processing stage, the system aims to largely reduce the search space to the lungs, where a segmentation of the lungs from the entire chest volume is usually required. Because of the high image contrast between lung fields and the surrounding body tissue, image intensity-based simple thresholding is effective, and is currently the most commonly used technique for lung segmentation. This paper proposes an adaptive solution to mitigate the difficulty of thresholding-based method in lung segmentation. Sufficient detection power for nodule candidates is inevitably accompanied by many (obvious) FPs. A rule-based filtering operation is often employed to cheaply and drastically reduce the number of obvious FPs, so that their influence on the computationally more expensive learning process can be eliminated. In general, FP reduction using machine learning has been extensively studied in the literature. Compared with unsupervised learning that aims to find hidden structures in unlabelled data, supervised learning, which aims to infer a function from labelled training data, is more frequently used to design a CADe system. Compared with the existing approaches, the morphology based lung cancer detection can be an alternative with either comparable detection performance and less computational cost, or comparable cost and better detection performance.

scholarly journals Solving product allocation problem (PAP) by using ANN and clustering

2021 ◽  
Vol 49 (1) ◽  
pp. 206-213
Author(s):  
Augustyn Lorenc ◽  
Małgorzata Kuźnar ◽  
Tone Lerher
Keyword(s):  
Input Data ◽  
New Classification ◽  
Product Allocation ◽  
New Approach ◽  
Classification Approach ◽  
Current State ◽  
Automatic Clustering ◽  
System Module ◽  
Artificial Neural Network Ann

Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In this paper, three variants were analyzed: the reference representing the current state, variant with product relocation by using ANN, and the variant with relocation by using automatic clustering. In the research over 380000 picks for almost 1600 locations were used. In the paper, the architecture of the system module for solving the PAP problem is presented. Presented research proved that using multi-criterion clustering can increase the efficiency of the order picking process.

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