Lung Cancer Classification Techniques

Abstract

Diagnosis of lung cancer with high accuracy rate is most difficult task to make remarkable vary in survival rate of patients. Different imaging techniques are used by radiologists and specialists to diagnose lung cancer such as Computer tomography (CT), X-ray and Magnetic Resonance Imaging (MRI). These methods help us to predict the malignant or benign or normal nodules present in the lungs. This proposed work is to build a lung classification system that can classify the images as malignant or benign or normal cases and give best accuracy for predicting lung cancer. In this “IQ_OTH/NCCD” lung cancer dataset is used which consist of total 1190 images of lung CT scans slices for 110 cases. CT scans in DICOM formats is utilized in this research work. In this proposed work by applying machine learning techniques such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), classify the malignant or normal or benign lung nodule cases and finally compare all the attained results. This work finds the accuracy of applied classification systems and finally CNN model outperforms with an accuracy of 98%. Accuracy of ANN model is observed to be 71%.