Recent trends in Computer Aided diagnosis of lung nodules in thorax CT scans

Naseem, Rabia; Alimgeer, Khurram Saleem; Bashir, Tariq

doi:10.1109/icieect.2017.7916548

Cited by 4 publications

(2 citation statements)

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“…But in supervised learning, one which provides a category label for objects is referred as training set. In unsupervised learning or clustering there is no explicit and the system forms clusters or natural groupings [31] of the input patterns [32]. Data mining techniques are used to find the nontechnical loss using classification algorithm.…”

Section: After Convolve Matrix Applied Figure 5 Fft and Convolution O...mentioning

confidence: 99%

Roc Curve Analysis of Different Hybrid Feature Descriptors Using Multi Classifiers

Kailasam,

Sankari,

Kumuthini

2023

AEJ

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Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine learning in an era of big data is that significant hierarchical relationships within the data can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) is used as feature descriptors in pulmonary malignancy prediction. Various feature descriptors such as Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradient (EXHOG) and Linear Binary Pattern (LBP) descriptors are analyzed with classifiers such as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for Computed Tomography (CT) The phenotype features of pulmonary nodules are important cues for identification. The nodule solidity is an important cue for white blob area identification. The method is analyzed in Lung Image Database Consortium (LIDC) dataset. Receivers Operating Characteristics (ROC) curves show the graphical summaries of detectors performance. It is proved that CNN based feature extraction with SVM classifier works well in pulmonary malignancy prediction.

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Section: After Convolve Matrix Applied Figure 5 Fft and Convolution O...mentioning

confidence: 99%

Roc Curve Analysis of Different Hybrid Feature Descriptors Using Multi Classifiers

Kailasam,

Sankari,

Kumuthini

2023

AEJ

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“…Based on the second order grey level co-occurrence matrix features the SVM will classify the images into normal or abnormal [10].A review paper RabiaNaseem et.al proposed an automated method to classify the lung nodules. Using the false positive reduction the lung nodule is classified as normal and abnormal region [11]. Qaisar Abbas proposed a deep learning algorithm to classify the lung nodule.…”

Section: Related Workmentioning

confidence: 99%

A Novel ASNO Segmentation Technique for Segmenting the JUXTA Vascular Region

Mathews¹,

Jeyakumar²

2018

IJET

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In this modern era the clinical laboratory has greater attention to produce an accurate result for every test particularly in the area of lung tumour. The lung tumour is very essential to detect as well as to follow the treatment of many diseases like benign, malignant etc. This paper is focusing on the segmentation part to find the juxta vascular region. For finding the juxta vascular region in lung three stages are used. First stage is the image acquisition here input lung image is read and then resized. Second stage is the image pre-processing here improved linear iterative clustering technique is used .Third stage is the segmentation here the adjustable surface normal overlap is used. While using the above stages the output for juxta vascular region in the segmentation part is segmented clearly. The juxta vascular region is not clearly found in the previous paper. The research gap for this paper is to find the juxta vascular region in the lung. This juxta vascular region is present in the right side of the lung. Using the Adjustable Surface Normal Overlap (ASNO) segmentation the juxta vascular region is segmented clearly.

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