LDNNET: Towards Robust Classification of Lung Nodule and Cancer Using Lung Dense Neural Network

Chen, Ying; Wang, Yerong; Hu, Fei; Feng, Longfeng; Zhou, Taohui; Zheng, Cheng

doi:10.1109/access.2021.3068896

Cited by 32 publications

(8 citation statements)

References 42 publications

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“…Chen et al [27] introduced robust Lung nodule classification and cancer detection system (LDNNET) consisted of dense neural network. LDNNET system is an adaptive architecture and comprised of dense block, batch normalization and dropout.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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show abstract

“…It uses a combination of both generative and discriminative representations. A deep learning system is designed in [5] for lung nodule classification. For effective training and more accuracy, batch normalization and dropout conditions are employed along with dense block architecture.…”

Section: Related Workmentioning

confidence: 99%

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Ramapraba¹,

Epsiba²,

Umapathy³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling and background subtraction. The detected lung regions are fed to the AC-GAN classifier to detect the nodules. It classifies the nodules into one of the two classes, i.e., binary classification (such as nodules or non-nodules). The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image. Three different optimization techniques, adaptive gradient optimization, root mean square propagation optimization, and Adam optimization are employed for optimizing the AC-GAN architecture. The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium (LIDC) database Computed Tomography (CT) scan images. The proposed AC-GAN-LCC system classifies ∼15000 CT slices (7310 non-nodules and 7685 nodules). It provides an overall accuracy of 98.8% on the LIDC database using Adam optimization by a 10-fold cross-validation approach.

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“…From equation (15), '𝐷𝐷 𝑎𝑐𝑐 ' is disease detection accuracy obtained by taking into two distinct factors, the samples involved in simulation '𝑆 𝑖 ' and samples accurately detected '𝑆 𝐴𝐷 '. It is measured in percentage (%).…”

Section: Scenario 1: Disease Detection Accuracymentioning

confidence: 99%

“…In [14], Histopathological image analysis was performed by fast deep belief neural network. Yet another method employing lung dense neural network was proposed in [15]. With this type of dense neural network robust classification of lung cancer was ensured with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

Padmanandam,

Khilar,

Pitla

et al. 2023

Preprint

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Lung cancer is a significant and frequent types of cancer globally both in patients and mortality rate. Computed Tomography (CT) test is efficient for early lung cancer detection due to potentiality in constructing lung images, generating nodules both in high resolution and tumor detection. But, early detection of lung cancer is obtained as difficult and time-consuming task. Also, detection of pulmonary nodules is a main method for early detection of lung cancer that can even heavily improve survival rate of lung cancer patients by reduced mortality rate to better level. Moreover, correct precision and incorrect detection of positive cancerous cell for lung nodules is both low and high. A lung cancer detection method using deep learning called, Residual Neural and Partial Derivative Multilayer Perceptron (RN-PDMP) is proposed. Initially the aid of lung cancer images acquired from IQ-OTH/NCCD Lung Cancer Dataset addresses class imbalance issue, therefore reducing the noise to a greater extent using Cross Entropy Focal Loss-based preprocessing model. Secondly preprocessed images as input, robust features are extracted by means of Census Transformed Residual Neural Network-based feature extraction algorithm. Finally, classification between normal, benign and malignant are made using Partial Derivative Multilayer Perceptron Deep Learning-based Classifier. The performance analysis of results describe to lung cancer detection model based on RN-PDMP integrating Forward Propagation-based Residual Learning has achieved a precision of 99% and recall of 97%. The results are comparatively better than existing methods in RN-PDMP method of lung cancer detection based on Partial Derivative-based back propagation is better recall for small samples and improves true positive pulmonary nodules.

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LDNNET: Towards Robust Classification of Lung Nodule and Cancer Using Lung Dense Neural Network

Cited by 32 publications

References 42 publications

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

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