Automatic Classification of Lung Nodules into Benign or Malignant Using SVM Classifier

Sasidhar, B; Geetha, G.; Khodanpur, B. I.; Babu, D. R. Ramesh

doi:10.1007/978-981-10-3156-4_58

Cited by 3 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wei and Cao [74] showed an accuracy of 91.8% and AUC of 0.986 with texture and semantic features. Sasidhar and Geetha [75] had 92% accuracy using only texture features. Surrounding regions of nodules are incorporated to take more context information in some studies and they result in better classification than using nodule regions alone [134].…”

Section: Discussionmentioning

confidence: 99%

“…These generic features such as SIFT, SURF, HOG, LBP, and Gabor filters etc. were adopted in References [17,41,43,50,70,71,72,73,74,75,76,94,96,107].…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…In Reference [75], the active contour method was first used to segment the lung regions and nodules, which were extracted by calculating eccentricity. GLCM was used to calculate the Haralick texture features.…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…Some selected papers are based on user-defined features, including shape [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60], texture [29,33,34,35,36,37,38,39,41,44,45,46,47,48,50,54,59,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,…”

Section: Main Process Introductionmentioning

confidence: 99%

“…The main methods for classifier construction are machine learning-based, which uses the training algorithm and labeled data. Many kinds of classifiers are used in selected papers, such as those based on support vector machines (SVM) [29,34,35,36,37,39,41,46,47,49,62,63,64,65,70,71,75,80,81,92,94,96,99,100,101,102,107,110,115,130], bayesian classifier [35,40,46,62,98,117], rule based [106], k-means [57,58,86], linear discriminant analysis (LDA) [33,46,54,57,58,68,72,76,82,88], artificial neural networks (ANNs) [44,45,50,55,62,66,69,77,78,79,104,112,121,122], convolutional Neural Networks (CNNs) […”

Section: Main Process Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Wang

Mao

Wang

et al. 2019

Sensors

View full text Add to dashboard Cite

Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.

show abstract

Section: Discussionmentioning

confidence: 99%

“…These generic features such as SIFT, SURF, HOG, LBP, and Gabor filters etc. were adopted in References [17,41,43,50,70,71,72,73,74,75,76,94,96,107].…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

Section: Analysis Of Selected Workmentioning

confidence: 99%

Section: Main Process Introductionmentioning

confidence: 99%

Section: Main Process Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Wang

Mao

Wang

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification

Liu

Cao

et al. 2020

J Digit Imaging

View full text Add to dashboard Cite

Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. ( 3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.

show abstract