VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.

Muhammad Attique Khan,David Taniar,Usman Tariq,Jnyana Ranjan Mohanty,Robertas Damaševičius,Suresh Chandra Satapathy,Venkatesan Rajinikanth

doi:10.3390/diagnostics11122208

Abstract

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

Highlights

IntroductionHealth Organization (WHO) report indicated that around 1.76 million deaths have occurred globally in 2018 due to lung cancer [1]
Lung cancer/nodule is one of the severe abnormalities in the lung, and a WorldHealth Organization (WHO) report indicated that around 1.76 million deaths have occurred globally in 2018 due to lung cancer [1]
Noninvasive radiological techniques are commonly adopted in initial level lung nodule detection using computed tomography (CT) images, and, several lung nodule detection works are already proposed in the literature [4,5,6] which involve the use of traditional signal processing and texture analysis techniques combined with machine learning classification [7], deep learning models [8,9], neural networks combined with nature-inspired optimization techniques [10,11] and ensemble learning [12]

Summary

Introduction

Health Organization (WHO) report indicated that around 1.76 million deaths have occurred globally in 2018 due to lung cancer [1]. Lung cancer/nodule is due to abnormal cell growth in the lung and, in most cases, the nodule may be cancerous/non-cancerous. The Olson report [2] confirmed that lung nodules can be categorized into benign/malignant based on their dimension (5 to 30 mm fall into the benign class and >30 mm is malignant). When a lung nodule is diagnosed using the radiological approach, a continuous follow-up is recommended to check its growth rate. The follow-up procedure can continue for up to two years and, along with non-invasive radiographic imaging procedures, other invasive methodologies, such as bronchoscopy and/or tissue biopsy, can be suggested to confirm the condition and harshness of the lung nodules in a patient [3]. The aims of this research are to construct a Deep Learning (DL) supported scheme to segment the lung nodule segment from the CT image slice with better accuracy and classify the considered CT scan images into normal/nodule class with improved accuracy using precisely selected deep and handcrafted features

Objectives

Methods

Results

Conclusion