scholarly journals Performance analysis of texture characterization techniques for lung nodule classification

2022 ◽  
Vol 2161 (1) ◽  
pp. 012045
Author(s):  
Ishan Devdatt Kawathekar ◽  
Anu Shaju Areeckal
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Machine Learning Approach ◽  
Multi Class Classification ◽  
Semi Solid ◽  
Occurrence Matrix ◽  
Texture Characterization ◽  
Nodule Classification ◽  
Very High

Abstract Lung cancer ranks very high on a global index for cancer-related casualties. With early detection of lung cancer, the rate of survival increases to 80-90%. The standard method for diagnosing lung cancer from Computed Tomography (CT) scans is by manual annotation and detection of the cancerous regions, which is a tedious task for radiologists. This paper proposes a machine learning approach for multi-class classification of the lung nodules into solid, semi-solid, and Ground Glass Object texture classes. We employ feature extraction techniques, such as gray-level co-occurrence matrix, Gabor filters, and local binary pattern, and validate the performance on the LNDb dataset. The best performing classifier displays an accuracy of 94% and an F1-score of 0.92. The proposed approach was compared with related work using the same dataset. The results are promising, and the proposed method can be used to diagnose lung cancer accurately.

scholarly journals Do we need to see to believe?—radiomics for lung nodule classification and lung cancer risk stratification

2020 ◽  
Vol 12 (6) ◽  
pp. 3303-3316
Author(s):  
Ali Khawaja ◽  
Brian J. Bartholmai ◽  
Srinivasan Rajagopalan ◽  
Ronald A. Karwoski ◽  
Cyril Varghese ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Lung Cancer Risk ◽  
Lung Nodule ◽  
Nodule Classification

scholarly journals Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

2021 ◽  
Vol 38 (4) ◽  
pp. 1103-1112
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Hye-Jin Kim
Keyword(s):  
Lung Cancer ◽  
Patient Survival ◽  
Lung Nodule ◽  
Activation Function ◽  
Cancer Classification ◽  
Systematic Evaluation ◽  
Lung Nodules ◽  
Nodule Detection ◽  
Lung Cancer Therapy ◽  
Nodule Classification

The leading cause of cancer-related death globally has been identified as lung cancer. Early lung nodule detection is critical for lung cancer therapy and patient survival. The Gard Cam++ Class Activation Function is used with a squeeze-and-excite network to provide a revolutionary method for differentiating malignant from benign lung nodules on CT scans. The new SENET (Squeeze-and-Excitation Networks) Grad Cam++ module, which combines the features calibration and discrimination benefits of SENET, has been shown to have a substantial potential for improving feature discriminability in lung cancer classification. According to the publicly available LUng Nodule Analysis 2016 (LUNA16) database, when assessed on 1230 nodules, the technique achieved an AUC of 0.9664 and an accuracy of 97.08% (600 malignant and 630 benign). The favorable results demonstrate the robustness of our technique to nodule classification, which we anticipate will be valuable in the future. The technology's objective is to aid radiologists in evaluating diagnostic data and differentiating benign from malignant lung nodules on CT images. To our knowledge, no systematic evaluation of SENET usefulness in classifying lung nodules has been done.

scholarly journals Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

2021 ◽  
pp. 435-439
Author(s):  
Rajani Kumari ◽  
C. Thanuja ◽  
K. Sai Thanvi ◽  
K. Lakshmi ◽  
U. Lavanya
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
Lung Nodules ◽  
Shape Features ◽  
Rule Based ◽  
Occurrence Matrix

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

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

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2208
Author(s):  
Muhammad Attique Khan ◽  
Venkatesan Rajinikanth ◽  
Suresh Chandra Satapathy ◽  
David Taniar ◽  
Jnyana Ranjan Mohanty ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
Detection Accuracy ◽  
Learning Framework ◽  
Pet Ct ◽  
Nodule Mining ◽  
Serially Concatenated ◽  
Occurrence Matrix

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.

Lung Nodule Classification of CT Images Based on the Deep Learning Algorithms

2021 ◽  
Author(s):  
Hassan Mkindu ◽  
Longwen Wu ◽  
Yaqin Zhao ◽  
Liang Zhao
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Lung Nodule ◽  
Ct Images ◽  
Nodule Classification

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

scholarly journals Multi-Level Cross Residual Network for Lung Nodule Classification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2837 ◽  
Author(s):  
Juan Lyu ◽  
Xiaojun Bi ◽  
Sai Ho Ling
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Binary Classification ◽  
Lung Nodule ◽  
Disease Diagnosis ◽  
Lung Nodules ◽  
Lung Cancer Patients ◽  
Multi Scale ◽  
Multi Level ◽  
Nodule Classification

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

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