Artificial neural network-based classification system for lung nodules on computed tomography scans

Dandıl, Emre; Çakıroğlu, Murat; Ekşi, Ziya; Özkan, Murat; Kurt, Özlem Kar; Canan, Arzu

doi:10.1109/socpar.2014.7008037

Cited by 64 publications

(50 citation statements)

References 17 publications

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“…Several classical machine learning approaches have been previously used for automatic classification of digitised chest images [6,7]. For instance, in [8], three statistical features were calculated from lung texture to discriminate between malignant and benign lung nodules using a support vector machine classifier.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas

Abdelsamea

Gaber

2020

Preprint

381

338

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Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adopt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC ), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

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Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas

Abdelsamea

Gaber

2020

Preprint

381

338

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“…In recent years, motivated by the creditable performance of neural network in the fields of computer vision, applying the deep learning technique in medical image has become a main trend that shows promising results. Consequently, various CAD systems based on neural network have been proposed to implement the classification of lung nodules [7][8][9][10][11][12][13][14][15]. Compared to traditional CAD systems, neural network based systems can automatically extract high-level features from the original images by using different network structures.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Medical Images Using F&BGAN for Improved Lung Nodules Classification by Multi-Scale VGG16

Zhao

Zhu

et al. 2018

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Lung cancer is one of the highest causes of cancer-related death in both men and women. Therefore, various diagnostic methods for lung nodules classification have been proposed to implement the early detection. Due to the limited amount and diversity of samples, these methods encounter some bottlenecks. In this paper, we intend to develop a method to enlarge the dataset and enhance the performance of pulmonary nodules classification. We propose a data augmentation method based on generative adversarial network (GAN), called Forward and Backward GAN (F&BGAN), which can generate high-quality synthetic medical images. F&BGAN has two stages, Forward GAN (FGAN) generates diverse images, and Backward GAN (BGAN) is used to improve the quality of images. Besides, a hierarchical learning framework, multi-scale VGG16 (M-VGG16) network, is proposed to extract discriminative features from alternating stacked layers. The methodology was evaluated on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, with the best accuracy of 95.24%, sensitivity of 98.67%, specificity of 92.47% and area under ROC curve (AUROC) of 0.980. Experimental results demonstrate the feasibility of F&BGAN in generating medical images and the effectiveness of M-VGG16 in classifying malignant and benign nodules.

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“…Krewer et al (2013) proposed a methodology based on a combination of texture and shape features using Correlation-based Feature Subset Selection and KNN, with an accuracy rate of 90.91%. Dandil et al (2014) proposed a methodology based on texture features using Principal Component Analysis (PCA) and Artificial Neural Network (ANN), with an accuracy rate of 90.63%. Parveen and Kavitha (2014) proposed a methodology based on texture features using SVM, with a sensitivity rate of 91.38% and specificity rate of 89.56%.…”

Section: Introductionmentioning

confidence: 99%

Taxonomic indexes for differentiating malignancy of lung nodules on CT images

et al. 2016

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Introduction: Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods: This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results: The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion:The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.

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Artificial neural network-based classification system for lung nodules on computed tomography scans

Cited by 64 publications

References 17 publications

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Synthetic Medical Images Using F&BGAN for Improved Lung Nodules Classification by Multi-Scale VGG16

Taxonomic indexes for differentiating malignancy of lung nodules on CT images

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