A NOVEL CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS ON PULMONARY NODULE

Abstract

Medical images are an important part of a patient's health record, and they need data manipulation, processing, and handling by computers. As a result, medical data is a type of bigdata, and its analysis has become complex. Because manual disease diagnosis takes longer and produces less accurate results, it may result in incorrect treatment. Three DCNN architectures have been exploited and evaluated for tumor detection and classification. The sample image for the experimentation is chosen from Lung Image Database Consortium (LIDC) with Image Database Resource Initiative (IDRI) and Kaggle dataset which consists of normal and abnormal image. The experimental results of proposed DCNN classifier achieved best accuracy than the GoogleNet, AlexNet, Artificial neural network and support vector machine.

Keywords

• lung cancer
• dcnn
• lidc and googlenet
• alexnet