Lung Cancer Detection from Raw Computed Tomography Images Using a Novel Convolutional Neural Network Model

Abstract

Lung cancer is a common health problem in our country and in the world, and it ranks first in cancer-related deaths. Early diagnosis of lung cancer is of vital importance in terms of more informed progress about the course of the disease and survival of the patient. Recently, with the development of technology, artificial intelligence and deep learning-based systems; by using data obtained from medical imaging systems such as Computed Tomography (CT), Magnetic Resonance (MR), it provides some convenience to experts to diagnose the disease. In this study, a new Convolutional Neural Network (CNN) model is proposed to classify cancerous and normal lung CT images. The classification results of the proposed ESA model and the pre-trained ResNeXt deep learning model were compared. A publicly available lung CT images were used for training and testing of the models. In the training and testing stages of the model, the images were classified as raw without using any image preprocessing steps. As a result of the study, it has been observed that the proposed ESA model performs better than the ResNeXt architecture, with an accuracy of 99%.