Computer-aided diagnosis: a neural-network-based approach to lung nodule detection

Penedo, Manuel G.; Carreira, Marı́a J.; Mosquera, A.; Cabello, D.

doi:10.1109/42.746620

Cited by 177 publications

(70 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While many CAD schemes were reported for the detection of solid nodules in chest CT images and showed satisfactory performance (3)- (5) , few schemes were reported for the detection of GGO nodules due to their faint contrast and fuzzy margins, which complicate the detection process. For example, Kim et al presented an automated scheme based on artificial neural network utilizing texture features and Gaussian curve fitting features offering area under the Receiver Operating Characteristic (ROC) curve of 0.92 (2) .…”

Section: Rbfmentioning

confidence: 99%

Reduction of False Positives in a CAD System for GGO Nodule Detection by Means of Neural Classification and CT Coronal View Examination

2007

View full text Add to dashboard Cite

We have previously developed a CAD scheme for the detection of lung nodules of both types, solid and GGO nodules. The reported CAD scheme was applied on 715 transaxial CT images containing 25 GGO nodules. It was able to detect 23 nodules and missed 2 nodules achieving detection sensitivity of 92% with False Positive (FP) rate of 0.76 FP/slice.The purpose of this study is to investigate the possibility of further decreasing the number of FP findings reported by our CAD scheme. We will present a proposed neural network that can discriminate GGO nodules efficiently. Also we will benefit from the information revealed from the coronal view examination to reduce the FP rate.Radial Basis Functions (RBF) neural networks are mainly used for pattern classification and they are based on Cover 's theorem on the separability of patterns. This theorem states that nonlinearly separable patterns can be separated linearly if the pattern is cast nonlinearly into a higher dimensional space. Therefore an RBF network converts the input to a higher dimension after which it can be classified using only one layer of neurons with linear activation functions.In the training of the proposed RBF network, we adopted the concept of massive training proposed by Suzuki et al.. This concept, in principle, proposes using a large number of subimages extracted from the input images together with the teacher images containing the distribution for the likelihood of being a nodule. To obtain the output image, a scanning process is performed on the input image with the proposed network and then some score is defined to discriminate the nodular candidates. The algorithm of neural classification is shown in Fig. 1.The second stage of the discriminating procedure is the examination of the coronal CT image coressponding to the location of the assigned GGO nodule in the transaxial sectional image based on the fact that nodular candidates tend to appear in almost circular shapes in both sectional views, i.e. transaxial and coronal sections. Fig. 2 illustrates an example of a GGO nodule in both sectional views.We first apply the neural classification using the proposed RBF network on the transaxial CT images and then examine the corresponding coronal CT images. In our study, we analyzed 2100 transaxial CT images in addition to the corresponding 2142 coronal CT images. Table 1 shows the detection sensitivity and FP rate after applying the proposed discriminating procedure. It is obvious that the combination of both neural classification and coronal view examination improved the experimental results significantly. RBF Network Sliding im age window MemberIn this paper, we investigate a procedure for decreasing the number of false positive findings in a reported Computer Aided Diagnosis (CAD) system for the detection of Ground Glass Opacity (GGO) nodules in chest Computed Tomography (CT) images. The proposed procedure consists of two main stages. The first stage is the application of a Radial Basis Function (RBF) neural network on the CT images using a slidin...

show abstract

Section: Rbfmentioning

confidence: 99%

Reduction of False Positives in a CAD System for GGO Nodule Detection by Means of Neural Classification and CT Coronal View Examination

2007

View full text Add to dashboard Cite

show abstract

“…Penedo et al, [7] described a computer-aided diagnosis scheme which has two-level artificial neural network (ANN) architecture. In first level artificial neural network identifies suspicious regions in a low-resolution image.…”

Section: Previous Researchmentioning

confidence: 99%

Classification of Lung Cancer Nodules using SVM Kernels

Parveen¹,

Kavitha²

2014

IJCA

View full text Add to dashboard Cite

Support Vector Machines (SVM) is a machine learning method used for classifying the system. It analyses and identifies the categories using the trained data. It is widely used in medical field for diagnosing the disease. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction gray level co-occurrence Matrix (GLCM) is been used. Extracted features are classified using different kernels of Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images.

show abstract

“…Experimental results indicate that the segmentation method extracts lung surfaces automatically and accurately. Penedo et al (1998) put fourth Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. In this study, the authors have provided a computer-aided diagnosis system based on two-level Artificial Neural Network (ANN) architecture.…”

Section: Introductionmentioning

confidence: 99%

A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

Gomathi¹,

Thangaraj²

2010

American Journal of Applied Sciences

View full text Add to dashboard Cite

Problem statement: Computer Tomography (CT) has been considered as the most sensitive imaging technique for early detection of lung cancer. Approach: On the other hand, there is a requirement for automated methodology to make use of large amount of data obtained CT images. Computer Aided Diagnosis (CAD) can be used efficiently for early detection of Lung Cancer. Results: The usage of existing CAD system for early detection of lung cancer with the help of CT images has been unsatisfactory because of its low sensitivity and False Positive Rates (FPR). This study presents a CAD system which can automatically detect the lung cancer nodules with reduction in false positive rates. In this study, different image processing techniques are applied initially in order to obtain the lung region from the CT scan chest images. Then the segmentation is carried with the help of Fuzzy Possibility C Mean (FPCM) clustering algorithm. Conclusion/Recommendations: Finally for automatic detection of cancer nodules, Support Vector Machine (SVM) is used which helps in better classification of cancer nodules. The experimentation is conducted for the proposed technique by 1000 CT images collected from the reputed hospital.

show abstract

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection

Cited by 177 publications

References 17 publications

Reduction of False Positives in a CAD System for GGO Nodule Detection by Means of Neural Classification and CT Coronal View Examination

Reduction of False Positives in a CAD System for GGO Nodule Detection by Means of Neural Classification and CT Coronal View Examination

Classification of Lung Cancer Nodules using SVM Kernels

A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

Contact Info

Product

Resources

About