Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network

Background: Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system. Objectives: We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer. Methods: The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules. Results: The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively. Conclusion: The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.

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Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

Frontiers in Public Health ◽

10.3389/fpubh.2021.671070 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinglun Liang ◽

Guoliang Ye ◽

Jianwen Guo ◽

Qifan Huang ◽

Shaohui Zhang

Keyword(s):

Lung Cancer ◽

Early Stage ◽

Pulmonary Nodules ◽

Ct Images ◽

False Positives ◽

Classification Systems ◽

Cad System ◽

Nodule Detection ◽

Diagnosis Of Lung Cancer ◽

Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

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Segmentation of Lung Nodules in CT Scan Data

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch023 ◽

2017 ◽

pp. 601-613 ◽

Cited By ~ 1

Author(s):

Shehzad Khalid ◽

Anwar C. Shaukat ◽

Amina Jameel ◽

Imran Fareed

Keyword(s):

Lung Cancer ◽

Lung Nodule ◽

Clinical Importance ◽

Pulmonary Nodules ◽

Cad System ◽

Nodule Detection ◽

Scan Data ◽

Effective Domain ◽

Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

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Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

BioMed Research International ◽

10.1155/2019/6051939 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Yuya Onishi ◽

Atsushi Teramoto ◽

Masakazu Tsujimoto ◽

Tetsuya Tsukamoto ◽

Kuniaki Saito ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Lung Cancer ◽

Pulmonary Nodule ◽

Classification Accuracy ◽

Pulmonary Nodules ◽

Ct Images ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Benign Nodules

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images.

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Performance of a computer-aided diagnosis (CAD) system for the detection of pulmonary nodules at multidetector CT data in low dose lung cancer screening

Clinical Imaging ◽

10.1016/j.clinimag.2005.04.012 ◽

2005 ◽

Vol 29 (4) ◽

pp. 300

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Low Dose ◽

Multidetector Ct ◽

Pulmonary Nodules ◽

Lung Cancer Screening ◽

Cad System ◽

Computer Aided ◽

Ct Data ◽

Aided Diagnosis

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Segmentation of Lung Nodules in CT Scan Data

International Journal of Privacy and Health Information Management ◽

10.4018/ijphim.2015070104 ◽

2015 ◽

Vol 3 (2) ◽

pp. 66-77 ◽

Cited By ~ 2

Author(s):

Shehzad Khalid ◽

Anwar C. Shaukat ◽

Amina Jameel ◽

Imran Fareed

Keyword(s):

Lung Cancer ◽

Lung Nodule ◽

Clinical Importance ◽

Pulmonary Nodules ◽

Cad System ◽

Nodule Detection ◽

Scan Data ◽

Effective Domain ◽

Aided Diagnosis

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Pulmonary Nodule Classification with 3D Convolutional Neural Networks

10.5753/wvc.2019.7630 ◽

2019 ◽

Author(s):

Anthony E. A. Jatobá ◽

Lucas L. Lima ◽

Marcelo C. Oliveira

Keyword(s):

Neural Network ◽

Lung Cancer ◽

Neural Networks ◽

Early Detection ◽

Patient Survival ◽

Pulmonary Nodules ◽

Nodule Volume ◽

3D Cnn ◽

Nodule Classification

Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classiﬁcation of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a sensibility of 84.83%. Moreover, our results have shown that the ﬁrst slices of a nodule provide the best results and only ﬁve nodule slices are enough for a 3D CNN achieve its best results.

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Developing an Artificial Intelligence Project in your Radiology Department

Indian Journal of Musculoskeletal Radiology ◽

10.25259/ijmsr_50_2019 ◽

2020 ◽

Vol 2 ◽

pp. 58-61 ◽

Cited By ~ 1

Author(s):

Syed Junaid ◽

Asad Saeed ◽

Zeili Yang ◽

Thomas Micic ◽

Rajesh Botchu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Health Care ◽

Deep Learning ◽

Learning Algorithms ◽

Radiology Department ◽

Short Article ◽

Computing Power ◽

Road Map ◽

Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

Revista de Chimie ◽

10.37358/rc.20.6.8171 ◽

2020 ◽

Vol 71 (6) ◽

pp. 66-74

Author(s):

Younis M. Younis ◽

Salman H. Abbas ◽

Farqad T. Najim ◽

Firas Hashim Kamar ◽

Gheorghe Nechifor

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diesel Fuel ◽

Back Propagation ◽

Second Step ◽

Heating Value ◽

Network Training ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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