LUNG CANCER DETECTION USING SUPPORT VECTOR MACHINE
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Abstract
Medical Image processing approaches are now widely used for detection of numerous diseases. The present study is focused on the detection of lung cancer with more accuracy and sensitivity thus blending the data mining and image processing systems. Computed Tomography (CT) scan is one of the best specific and sensitive detection modalities producing cross-sectional images of any specific area of body. This work aims to design a machine learning model which can take lung-CT images as input and produce a classified output that detect the Lung Condition. The Images are pre-processed and watershed transform before it is applied to the model. Machine learning algorithm, Support Vector Machine (SVM) is implemented for this application and is effective in terms of specificity, accuracy and sensitivity. Overall, the model has accuracy of 65.16%. By this, it can be concluded that SVM classification can be used for the detection of lung cancer with much more accuracy if SVM classifier is trained with large datasets. In future, better implementation and hybrid models can detect the lung cancer early.
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Computed Tomography, Support Vector Machine, Datasets, Lung Cancer
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