Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

Wu, Panpan; Sun, Xuanchao; Zhao, Ziping; Wang, Haishuai; Pan, Shirui; Schuller, Björn

doi:10.1155/2020/8975078

Cited by 42 publications

(23 citation statements)

References 29 publications

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“…Adopting effective classification techniques can reduce FP detection and, thus, significantly improve the accuracy of nodule identification and reduce the difficulty of subsequent tasks. For example, Wu et al [ 78 ] developed a deep residual network to classify lung nodules. For this, the authors adopted a transfer-learning-based 50-layer Res-Net structure with global average pooling.…”

Section: Classificationmentioning

confidence: 99%

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Xiao

Huang

et al. 2022

Diagnostics

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Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.

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Section: Classificationmentioning

confidence: 99%

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Xiao

Huang

et al. 2022

Diagnostics

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“…Yang et al [24] had used CT scan images and initially identified the pulmonary parenchyma area by lung segmentation and used DenseNet for classification that gave an accuracy of 92% with an AUC of 0.98 on the test set. Wu et al [25] have used lung regions extracted as nodules and no nodules using radiologist's annotations and then used four different methods to classify between the two classes using 10-fold cross-validation. Authors tested on Curvelet and SVM, VGG19, Inception V3, and ResNet 50 and further obtained the best accuracy of 98.23% with AUC of 0.99.…”

Section: Background Literaturementioning

confidence: 99%

“…Deep Learning (DL) [15][16][17][18][19][20] is a branch of AI that was mostly used for COVID classification. There are many research studies using Transfer Learning (TL)-based [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

et al. 2021

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Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.

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“…Along with the TB classification, CNN based approaches found in the literature are also utilized for lung nodule classification from the entire input image [30], [31]. In [30] lung nodules are classified using an automated classification technique. The authors utilized the pre-trained CNN architecture i.e.…”

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

et al. 2021

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Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist's time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.

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Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

Cited by 42 publications

References 29 publications

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

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