Lung Nodule Classification of CT images Using Channel and Spatial Attention CNN with Bayesian Optimization

2021 ◽  
Author(s):  
Hassan Mkindu ◽  
Longwen Wu ◽  
Yaqin Zhao ◽  
Liang Zhao
Keyword(s):  
Spatial Attention ◽  
Lung Nodule ◽  
Ct Images ◽  
Bayesian Optimization ◽  
Nodule Classification

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.

Improving robustness of a deep learning-based lung-nodule classification model of CT images with respect to image noise

2021 ◽  
Author(s):  
Yin Gao ◽  
Jennifer Xiong ◽  
Chenyang Shen ◽  
Xun Jia
Keyword(s):  
Medical Image Analysis ◽  
Lung Nodule ◽  
Ct Images ◽  
Image Noise ◽  
Classification Model ◽  
Noise Power ◽  
Ct Image ◽  
Hinge Loss ◽  
Svm Model ◽  
Nodule Classification

Abstract Objective: Robustness is an important aspect to consider, when developing methods for medical image analysis. This study investigated robustness properties of deep neural networks (DNNs) for a lung nodule classification problem based on CT images and proposed a solution to improve robustness. Approach: We firstly constructed a class of four DNNs with different widths, each predicting an output label (benign or malignant) for an input CT image cube containing a lung nodule. These networks were trained to achieve Area Under the Curve of 0.891-0.914 on a testing dataset. We then added to the input CT image cubes noise signals generated randomly using a realistic CT image noise model based on a noise power spectrum at 100 mAs, and monitored the DNN’s output change. We defined $SAR_{5} (\%)$ to quantify the robustness of the trained DNN model, indicating that for $5\%$ of CT image cubes, the noise can change the prediction results with a chance of at least $SAR_{5} (\%)$. To understand robustness, we viewed the information processing pipeline by the DNN as a two-step process, with the first step using all but the last layers to extract representations of the input CT image cubes in a latent space, and the second step employing the last fully-connected layer as a linear classifier to determine the position of the sample representations relative to a decision plane. To improve robustness, we proposed to retrain the last layer of the DNN with a Supporting Vector Machine (SVM) hinge loss function to enforce the desired position of the decision plane. Main results: $SAR_{5}$ ranged in $47.0\sim 62.0\%$ in different DNNs. The unrobustness behavior may be ascribed to the unfavorable placement of the decision plane in the latent representation space, which made some samples be perturbed to across the decision plane and hence susceptible to noise. The DNN-SVM model improved robustness over the DNN model and reduced $SAR_{5}$ by $8.8\sim 21.0\%$. Significance: This study provided insights about the potential reason for the unrobustness behavior of DNNs and the proposed DNN-SVM model improved model robustness.

scholarly journals Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

2020 ◽  
Vol 60 ◽  
pp. 101628 ◽  
Author(s):  
Yiming Lei ◽  
Yukun Tian ◽  
Hongming Shan ◽  
Junping Zhang ◽  
Ge Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Ct Images ◽  
Low Dose Ct ◽  
Nodule Classification ◽  
Activation Mapping

scholarly journals On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise

2020 ◽  
Vol 65 (24) ◽  
pp. 245037
Author(s):  
Chenyang Shen ◽  
Min-Yu Tsai ◽  
Liyuan Chen ◽  
Shulong Li ◽  
Dan Nguyen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Ct Images ◽  
Image Noise ◽  
Nodule Classification

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.

OP605 Artificial Intelligence Assisted Diagnosis Technology For Benign-Malignant Lung Nodule Classification On Computerized Tomography Images: A Meta-Analysis

2020 ◽  
Vol 36 (S1) ◽  
pp. 14-15
Author(s):  
Guo Huang ◽  
Yizhong Zhang ◽  
Di Xue
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Likelihood Ratio ◽  
Computerized Tomography ◽  
Meta Analysis ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
Assisted Diagnosis ◽  
Nodule Classification

IntroductionArtificial Intelligence (AI) is an important product of the rapid development of computer technology today. It has a far-reaching impact on the development of medical diagnostic technology especially in combination with medical imaging. The aim of this study was to analyze the diagnostic accuracy of AI-assisted diagnosis technology for classification of benign and malignant lung nodules on Computerized Tomography (CT) images.MethodsA meta-analysis was conducted of published research articles on diagnostic accuracy of AI-assisted diagnosis technology for lung nodules classification between 2010 and 2019 in the databases of PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and China Bio-medicine Database. Statistical analysis was performed with the software SAS 9.4 and Stata 12.0, and the summary receiver operating characteristic (SROC) curve was drawn to evaluate accuracy of the method.ResultsA total of 27 studies with 5,701 lung nodules were considered. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the curve of SROC for AI-assisted diagnosis technology for lung nodules classification respectively were 0.892 (95% confidence interval [CI]: 0.854–0.920), 0.876 (95% CI: 0.833–0.909), 7.190 (95% CI: 5.194–9.955), 0.124 (95% CI: 0.089–0.171), 58.102 (95% CI: 32.391–104.219) and 0.95 (95%CI: 0.92–0.96).ConclusionsOf note, several limitations should be considered when interpreting the findings of this meta-analysis. Data acquisition is not comprehensive enough because the language of the literature search was limited to Chinese and English. Furthermore,heterogeneity caused due to the difference of lung nodule size affected the study results. Despite these limitations, our study suggests that AI-assisted diagnosis technology for benign-malignant lung nodule classification on CT images obtains high diagnostic accuracy, and it can be used as a novel method to differentiate benign and malignant pulmonary nodules.

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