Lung nodule classification using artificial crawlers, directional texture and support vector machine

2017 ◽  
Vol 69 ◽  
pp. 176-188 ◽  
Author(s):  
Bruno Rodrigues Froz ◽  
Antonio Oseas de Carvalho Filho ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Rodolfo Acatauassú Nunes ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Lung Nodule ◽  
Support Vector ◽  
Directional Texture ◽  
Nodule Classification

Lung nodule classification in frequency domain using support vector machines

2012 ◽  
Author(s):  
Hiram Madero Orozco ◽  
Osslan Osiris Vergara Villegas ◽  
Leticia Ortega Maynez ◽  
Vianey Guadalupe Cruz Sanchez ◽  
Humberto de Jesus Ochoa Dominguez
Keyword(s):  
Support Vector Machines ◽  
Frequency Domain ◽  
Lung Nodule ◽  
Support Vector ◽  
Vector Machines ◽  
Nodule Classification

scholarly journals Lung Tumor Segmentation Using Marker-Controlled Watershed and Support Vector Machine

2021 ◽  
Vol 12 (2) ◽  
pp. 51-64
Author(s):  
Surbhi Vijh ◽  
Rituparna Sarma ◽  
Sumit Kumar
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Tumor Detection ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Interventional Treatment ◽  
Machine Learning Classification ◽  
Tumor Stages

The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and exploring the abnormalities issues in tumor detection. The early stage of lung tumor detection is extremely important in medical research field. The proposed work uses image processing segmentation technique for detection of lung tumor and the support vector classifier learning technique for predicting stage of tumor. After performing preprocessing and segmentation the features are extracted from region of lung nodule. The classification is performed on dataset acquired from national cancer institute for the evaluation of lung cancer diagnosis. The multi-class machine learning classification technique SVM (support vector machine) identifies the tumor stage of lung dataset. The proposed methodology provides classification of tumor stages and improves the decision-making process. The performance is evaluated by measuring the parameters namely accuracy, sensitivity, and specificity.

scholarly journals Lung Nodule Detection of CT and Image-Based GLCM and RLM CT Scan Using the Support Vector Machine (SVM) Method

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Zaimah Permatasari ◽  
Mauridhi Hery Purnomo ◽  
I Ketut Eddy Purnama
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Medical Personnel ◽  
Ct Images ◽  
Support Vector ◽  
Nodule Detection ◽  
The Status ◽  
Keywords Cancer

Lung cancer is the most common cause of cancer death globally. Early detection of lung cancer will greatly beneficial to save the patient. This study focused on the detection of lung cancer using classification with the Support Vector Machine (SVM) method based on the features of Gray Level Co-occurrence Matrices (GLCM) and Run Length Matrix (RLM). The lung data used were obtained from the Cancer imaging archive Database, consisting of 500 CT images. CT images were grouped into 2 clusters, including normal and lung cancer. The research steps include: image processing, region of interest segmentation, and feature extraction. The results indicate that the system can detect the CT-image of SVM classification where the default parameter only provides an accuracy of 85.63%. It is expected that the results will be useful to help medical personnel and researchers to detect the status of lung cancer. These results provide information that detection of lung nodules based on GLCM and RLM features that can be detected is better. Furthermore, selecting parameters C and γ on SVM. Keywords: cancer, nodule, support vector machine (SVM).

Lung Nodule Classification in CT Thorax Images Using Support Vector Machines

2013 ◽  
Author(s):  
Hiram Madero Orozco ◽  
Osslan Osiris Vergara Villegas ◽  
Humberto de Jesus Ochoa Dominguez ◽  
Vianey Guadalupe Cruz Sanchez
Keyword(s):  
Support Vector Machines ◽  
Lung Nodule ◽  
Support Vector ◽  
Vector Machines ◽  
Nodule Classification

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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