Prediction Lung Cancer– In Machine Learning Perspective

Abstract

Past years have experienced increasing mortality rate due to lung cancer and thus it becomes crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved and accurate prediction also can help the doctors start their treatment. Computed tomography plays a vital role in ensuring the condition of tumor that by checking the size of tumor, location of tumor, etc. In this paper, we have proposed a framework for prediction of cancer at an early stage so that many lives that are in an endangered situation could be revived. Basically, our focus is on two domains of computer science that is Digital Image Processing acronymed DIP and Machine Learning. Digital image processing is well-known for the phase of preprocessing the image. In the further stage, the pre-processed image is exposed to segmentation phase and then the segmented image is passed for feature extraction and finally the extracted features are trained using machine learning classification algorithms like SVM (Support Vector Machines), Random Forest, ANN (Artificial Neural Network) . Based on the classification results obtained, prediction is made whether the tumor is benign or malignant. The inevitable parameters such as accuracy, Recall and precision are calculated for determining which algorithm has the highest predictive accuracy.