Lung Cancer Detection Using Marker Controlled Watershed with SVM

Lung cancer is one of the most dangerous diseases and prediction of it, is the most challenging problem nowadays. Most of the cancer cells are overlapped with each other. It is hard to detect the cells but also essential to identify the presence of cancer cells in the early stage. Early detection of lung cancer may reduce the death rate. In this study, we used the Grey Level Co-occurrence Matrix (GLCM) to extract the feature of cancer affected lung image and then Support Vector Machine (SVM) has been used to detect normal and abnormal lung cells after implementing the features. Our experimental evaluation using MATLAB demonstrates the efficient performance of the proposed system and in the result. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 24-30

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Lung Cancer Detection using Nearest Neighbour Classifier

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1458.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 3641-3645

Keyword(s):

Lung Cancer ◽

Cancer Cells ◽

Cancer Detection ◽

Early Stage ◽

Second Step ◽

Nearest Neighbour ◽

Cancer Data ◽

Diagnosis Of Cancer ◽

Nearest Neighbour Classifier ◽

Lung Cancer Detection

One of the most precarious diseases is lung cancer. Lung cancer detection is one of the main challenging dilemma nowadays. Most of the cancer cells are overlies with each other. It is tough to detect the cell but also important to identify the existence of cancer cells in the early stage unless unable to prevent. According to 2018 reports, 17 million new lung cancer cases are identified worldwide. The Computer Tomography can be used for diagnosis of cancer with image processing. In this research, we proposed two steps of process for diagnosing the presence of cancer either benign or malignant. In the first step, features are extracted by using GLCM. In the second step, the lung cancer cells are classified either benign or malignant by using Nearest Neighbour classifier. Experimental results demonstrated that the proposed approach performance is 98.76% classification accuracy for diagnosing the lung cancer data.

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Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods

2020 International Electronics Symposium (IES) ◽

10.1109/ies50839.2020.9231663 ◽

2020 ◽

Author(s):

Qurina Firdaus ◽

Riyanto Sigit ◽

Tri Harsono ◽

Anwar Anwar

Keyword(s):

Lung Cancer ◽

Support Vector Machine ◽

Ct Scan ◽

Cancer Detection ◽

Support Vector ◽

Gray Level ◽

Occurrence Matrix ◽

Lung Cancer Detection

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Green synthesis of nanomaterials for early lung cancer detection

Journal of Cancer Science and Therapeutics ◽

10.36879/jcst.18.000102 ◽

2018 ◽

pp. 1-1

Keyword(s):

Lung Cancer ◽

Green Synthesis ◽

Cancer Detection ◽

World Wide ◽

Early Stage ◽

Age Groups ◽

Early Lung Cancer ◽

Lung Cancer Detection ◽

Synthesis Of Nanomaterials

Lung cancer is the foremost cause of cancer-related deaths world-wide [1]. It affects 100,000 Americans of the smoking population every year of all age groups, particularly those above 50 years of the smoking population [2]. In India, 51,000 lung cancer deaths were reported in 2012, which include 41,000 men and 10,000 women [3]. It is the leading cause of cancer deaths in men; however, in women, it ranked ninth among all cancerous deaths [4]. It is possible to detect the lung cancer at a very early stage, providing a much higher chance of survival for the patients.

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