Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification

2020 ◽  
Author(s):  
Panagiotis Gonidakis ◽  
Bart Jansen ◽  
Jef Vandemeulebroucke
Keyword(s):  
Lung Nodule ◽  
3D Cnn ◽  
Nodule Classification

scholarly journals 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Yung-Cheol Byun
Keyword(s):  
Low Dose ◽  
Binary Classification ◽  
Lung Nodule ◽  
Fine Grained ◽  
Internal Working ◽  
Nodule Detection ◽  
Classification Tasks ◽  
3D Cnn ◽  
Nodule Classification ◽  
Fold Cross Validation

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

Lung nodule classification using combination of CNN, second and higher order texture features

2021 ◽  
pp. 1-9
Author(s):  
Amrita Naik ◽  
Damodar Reddy Edla
Keyword(s):  
Malignant Tumors ◽  
Early Stage ◽  
Lung Nodule ◽  
Texture Features ◽  
Svm Classifier ◽  
Softmax Classifier ◽  
Spatial Features ◽  
High Level ◽  
Learning Architectures ◽  
Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

Lung nodule classification based on shape distributions

2016 ◽  
Author(s):  
Valéria P. M. Fernandes ◽  
Rodrigo F. A. Kanehisa ◽  
Geraldo Braz ◽  
Aristófanes C. Silva ◽  
Anselmo C. de Paiva
Keyword(s):  
Lung Nodule ◽  
Nodule Classification
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