Extraction with Map-Reduce Framework and Correlation-based Feature Selection in Lung Cancer Towards Big Data

AbstractBackground/objectives: To extract nucleus and cytoplasm that intend to optimize features in high-dimensional images such as all types of raw sputum cells. To calculate following features efficiently: Area, Perimeter, Intensity, NC Ratio, and Circularity.Methods/Statistical analysis: To take results in proposed stride, we introduced map-reduce framework for separating similar cells from sputum cell images that have been collected from Microscope lab images with intended magnification and staining. To avoid model learn from irrelevant features, feature selection methods with correlation-based feature selection contributes appropriate features that are then fed for classification. Features here converted to vectors for the estimation of symmetric uncertainty, correlationbased approach.Findings: Performance evaluation metrics checks into the contribution to measure it’s out coming performance. Even though lot of works relied on feature extraction, our work combines feature extraction with map-reduce framework which improves accuracy for classification. Our proposed method makes extraction of nucleus and cytoplasm easier than other methods. Optimized performance assured in proposed feature selection.Novelty/ applications: Eventual accuracy for every feature in proposed stride improves than other existing works. In addition, ROC curves proves higher true positive rate even in increased datasets. Another significant innovation in our work is map-reduce framework applies in images to sort cells with respect to staining.Keywords: Health Care, Correlation, Classification, Big Data, Mapreduce, Sputum.