Lung Cancer Detection using Textural Features

Lung cancer is one of the leading causes of death worldwide, and early detection is crucial for curtailing the death rate of the disease. In this work detection of the lung cancer using textural features from computed tomography (CT) scan images. The extracted textural features are ABD and GLCM were then used to train various machine learning models, including support vector machines (SVM), decision trees, and random forests. The proposed algorithms are implemented using Scikit Learn Anaconda and the obtained accuracy is superior compared to the existing algorithms in literature.