A Deep Learning Technique For Lung Nodule Classification Based on False Positive Reduction - Ahmed, Hunar Abubakir; Mahmood, Sozan Abdulla (Sulaimani Journal for Pure and Applied Sciences, 2019)
Cancer is of the major reasons of human death universally, one of the deadliest types of cancer is lung cancer, that causes the highest rate of the dead in both genders combined. Detecting lung cancer in early stage does not guarantee the survive of the patient’s life but it can reduce the mortality ratio by a high degree, early detection mainly includes screening unhealthy human’s lung using most valuable imaging modality which is CT scan. Classifying nodules in lung CT images adopting an automatic computer system become a necessary task due to a huge number of situations every day to help human expert’s in decision making procedure. Over the past few years, a numerous computer system is presented, each done a certain task such as detecting, segmenting, and classifying lung tumors using dissimilar algorithms. The objective of this study is to design an automated lung nodule classification system using two distinct deep learning architectures which are Network In Network (NIN) and standard Convolution Neural Network (CNN). The two models are trained and tested using 13,500 2D cubes around the nodule location that obtained from LUNA16 dataset, the database consists of 888 3D CT scans with annotation file determined a nodule position in every scan. The models are trained with a diverse cube size and hyperparameters in order to develop a high-performance structure for each model. The experimental results showed that best achieved scores for NIN are accuracy 90%, precision 99%, recall 68%, and false positive rate 0.06%, but for the typical CNN are accuracy 90%, precision 85%, recall 85%, and false positive rate 7.52%.
Keywords: lung cancer; deep learning; convolutional neural network; LUNA16; classification