A Review on Computer Aided Detection and Diagnosis of Lung Cancer Nodules

Parveen, Shahida; Kavitha, C.

doi:10.24297/ijct.v3i3a.2944

Cited by 24 publications

(12 citation statements)

References 41 publications

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“…Shaik Parveen and Kavitha reviewed and conferred numerous preprocessing ways to reinforce the CT respiratory organ images, section wise approaches to segment the respiratory organ portion and classification approaches to classify respiratory organ nodules. Faizal Khan and Kannan explained numerous image segmentation ways.…”

Section: Related Workmentioning

confidence: 99%

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

Paulraj¹,

Chelliah²,

Chinnasamy³

2019

Int J Imaging Syst Tech

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Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C‐means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C‐means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.

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Section: Related Workmentioning

confidence: 99%

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

Paulraj¹,

Chelliah²,

Chinnasamy³

2019

Int J Imaging Syst Tech

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“…Feature Selection plays a major role in pattern recognition problems such as image classification [22]. Feature Selection is used to reduce the dimensionality of feature space which will improve classification accuracy and time consumption can be reduced.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In order to evaluate the proposed CAD system, some of the performance measures is to be taken place [22]. A true positive denotes abnormal lung nodule is accurately identified as abnormal.…”

Section: Performance Measuresmentioning

confidence: 99%

CAD system: a content based image retrieval approach for pulmonary nodule detection in CT images

Ram¹,

Shylaja²,

Sahayadhas³

2018

IJET

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This paper proposes Computerized Aided Detection System (CAD) which uses Content Based Image Retrieval (CBIR) to detect cancer nodules present in an image. The CAD system is concerned for the radiologists to identify lung cancer at premature stages, which are very tiny nodules that are not able to seen by naked eye. In modern years, Image processing techniques play a key role in predicting diseases at early stages in particular in various cancer types such as liver cancer, breast cancer etc. This paper comprises of four steps: i) preprocessing an image in order to lessen the noise level and the accuracy of the image is to be improved, so that the accuracy in detection will be higher. ii) The image is segmented based on Marker-Controlled Watershed Segmentation. iii) The features of the nodules present in the image are extracted using GLCM. iv) The nodules are classified based on the extracted features using KNN classifier. The Content Based Image Retrieval Technique is used which is used to redeem query based images in the database by combining feature extraction and similarity matching methods. For experimentation of proposed technique, CT images are used which are extracted from Lung Image Database Consortium database (LIDC).

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“…Computer aided detection (CAD) can play an important role in aiding early detection of the cancer . Because of their rapid growth and increasing reliability of medical imaging technologies, CAD systems are critical for objective diagnosis and timely early detection.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic detection of lung nodules in CT images using a hybrid feature set

et al. 2017

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Purpose The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. Method The proposed method starts with preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K‐Nearest‐Neighbor (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class‐wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k‐fold cross‐validation scheme. Results The overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 false positives per scan. Conclusions It is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives.

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A Review on Computer Aided Detection and Diagnosis of Lung Cancer Nodules

Cited by 24 publications

References 41 publications

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

CAD system: a content based image retrieval approach for pulmonary nodule detection in CT images

Fully automatic detection of lung nodules in CT images using a hybrid feature set

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