In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features.
Predicting Lung Cancer Using Machine Learning Techniques
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Description
In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images. Gray Level Co-event Matrix (GLCM) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increase survival rate. The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In the proposed work, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features.
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