This paper conducts a correlation review of classification algorithm using some free available data mining and knowledge discovery tools such as WEKA, Rapid miner, Tanagra, Orange and Knime. The accuracy of classification algorithm like Decision tree, Decision Stump, K-Nearest Neighbor and Naïve Bayes algorithm have been compared using all five tools. Indian Liver Patient DataSet is used for testing the Classification algorithm in order to classify the people with and without Liver disorder.
Accurate detection of malignant tumor on lung computed tomography scans is crucial for early diagnosis of lung cancer and hence the faster recovery of patients. Several deep learning methodologies have been proposed for lung tumor detection, especially the convolution neural network (CNN). However, as CNN may lose some of the spatial relationships between features, we plan to combine texture features such as fractal features and gray-level co-occurrence matrix (GLCM) features along with the CNN features to improve the accuracy of tumor detection. Our framework has two advantages. First it fuses the advantage of CNN features with hand-crafted features such as fractal and GLCM features to gather the spatial information. Second, we reduce the overfitting effect by replacing the softmax layer with the support vector machine classifier. Experiments have shown that texture features such as fractal and GLCM when concatenated with deep features extracted from DenseNet architecture have a better accuracy of 95.42%, sensitivity of 97.49%, and specificity of 93.97%, and a positive predictive value of 95.96% with area under curve score of 0.95.
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