Lung Nodule Detection and Classification
نویسنده
چکیده
Detection of malignant lung nodules in chest radiographs is currently performed by pulmonary radiologists, potentially with the aid of CAD systems. Recent advancements in convolutional neural network (CNN) models have improved image classification and detection for many tasks, but there has been little exploration of their use for nodule detection in chest radiographs. In this paper we explore using a ResNet CNN model with transfer learning to classify complete chest radiographs as non-nodule, benign nodule, or malignant nodule, and to localize the nodule, if present. The model is able to classify radiographs as nodule or nonnodule with 92% sensitivity and 86% specificity, but is less able to distinguish between benign and malignant nodules. The model is also able to determine the general nodule regions but is unable to determine exact nodule locations.
منابع مشابه
طراحی سیستم کمک تشخیص کامپیوتری نوین به منظور شناسایی ندولهای ریوی در تصاویر سیتی اسکن
Background: Lung diseases and lung cancer are among the most dangerous diseases with high mortality in both men and women. Lung nodules are abnormal pulmonary masses and are among major lung symptoms. A Computer Aided Diagnosis (CAD) system may play an important role in accurate and early detection of lung nodules. This article presents a new CAD system for lung nodule detection from chest comp...
متن کاملAutomated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy
Objective(s): Positron emission tomography/computed tomography (PET/CT) examination is commonly used for the evaluation of pulmonary nodules since it provides both anatomical and functional information. However, given the dependence of this evaluation on physician’s subjective judgment, the results could be variable. The purpose of this study was to develop an automated scheme for the classific...
متن کاملAutomatic Lung Cancer Detection and Diagnosis Using Hand Crafted and Deep Learning Features
This paper presents a lung nodule detection and classification system which utilizes a combination of hand crafted and deep learning features. Hand crafted features were obtained from modified methods of bag of frequencies, and taxonomic indices. We included a robust radius estimation algorithm that resulted in an average error of 1.29 pixels. Hand crafted features were obtained from 3D low dos...
متن کاملDeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant). Considering the 3D nature of lung CT data and the compactness of dual path networks (DPN), two deep 3D DPN ar...
متن کاملDeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to eff...
متن کاملLung Nodule Detection Using Hybrid Classifier
Lung cancer is one of the most injurious forms of cancer, which is the leading cause of cancer death in many regions of the world. Early detection of lung nodule is essential in reducing life victim. Detection of lung cancer at early stage is not an easy task. Survival rates in lung cancer vary significantly by stage; overall, less than 15% of newly diagnosed patients will survive for 5 years. ...
متن کامل