Abstract
Background/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.
Highlights
Lung cancer confronts with lots of people rapidly
Computed tomography images and sputum cell images engaged with lung cancer prediction and classification
Other feature selection methods include Chi-square, Recursive Feature Elimination (RFE), Random Forest whose nature selects based on filter and wrapper methods without any feature transformation
Summary
Lung cancer confronts with lots of people rapidly. Because many types of lung cancer grow quickly and spread rapidly and the lungs are vital organs, early detection and prompt treatment—usually surgery to remove the tumor—is critical. Medical diagnosis prompts several approaches to detect and cure lung cancer. Computed tomography images and sputum cell images engaged with lung cancer prediction and classification. The quality of images perhaps becomes fascinating features to predict lung cancer, which achieves through some image processing techniques. The mode of classifying lung cancer proceeds with the extraction of features such as area, perimeter, eccentricity followed by feature selection methods. Inconsistency removes by underlying map-reduce framework in various types of sputum images such as eosinophilia, bronchial mucus, squamous carcinoma cells and fed to feature extraction using MATLAB. Feature selection and classification work in the ML-PYSPARK environment for parallel processing of a large number of datasets
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