Lung nodule segmentation and detection in computed tomography

El-Regaily, Salsabil Amin; Salem, Mohammed A.-M.; Aziz, Mohamed Mostafa; Roushdy, Mohamed

doi:10.1109/intelcis.2017.8260029

Cited by 24 publications

(13 citation statements)

References 25 publications

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“…Please note that, "TA" indicates the traditional algorithm, "ML" indicates the machine learning, and "CNN" indicates the convolution neural networks. In addition, "V -fps=n" means that when there are n false positives per scan, the corresponding recall rate is smaller than the value V. For example, the corresponding results of El-Regaily (V=0.705 -, n=4) indicate that when there are 4 false positives per scan, the corresponding recall rate is less than or equal to 0.705 (The original paper [15] only shows that when there are 4.1 false positives per scan, the corresponding recall rate is 0.705).…”

Section: Experimental Comparisonmentioning

confidence: 99%

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction.Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset.Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance. Keywords: lung nodule detection; UNet; 3D CNN; ensemble learning;computer-aided diagnosis methods in the following aspects: 1) using the improved UNet segmentation model for lung nodule detection; 2) for the segmentation model training, we propose a new sampling strategy and an offline hard mining training approach; 3) we propose a cascade prediction method different from the traditional prediction method; 4) build three 3D CNN classification networks based on the dual pooling method; 5) design a data augmentation method based on random mask. MethodsThe lung nodule detection framework proposed in this paper is divided into two stages. The first stage: the detection of candidate nodules, which is based on the UNet architecture to achieve the detection of candidate nodules by segmenting suspicious nodules. The second stage: the reduction of false positive nodules, which is based on the 3DCNN architecture to eliminate false positive nodules through the integration of multiple models. The overall architecture of the proposed lung nodule detection method is shown in Fig. 2.

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Section: Experimental Comparisonmentioning

confidence: 99%

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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“…From experiments, we have achieved 99.23% accuracy which is significantly better than the previously reported score as 96.6% [2],70.5% [9],88.0% [14],98.9% [18],97.2% [32],93.25% [28]. The resultant sensitivity rate is 96.875% of different classifiers which is also dominant in terms of sensitivity because the sensitivity is much better than others.In previous methodologies sensitivity rate is less then our results.…”

Section: Resultsmentioning

confidence: 64%

“…The resultant sensitivity rate is 96.875% of different classifiers which is also dominant in terms of sensitivity because the sensitivity is much better than others.In previous methodologies sensitivity rate is less then our results. Whereas the sensitivity of other methodologies are 96.6% [2], 77.7% [9], 84.6% [14], 98.4% [18], 96.0% [32], 93.12% [28].…”

Section: Resultsmentioning

confidence: 93%

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

Meraj¹,

Hassan²,

Zahoor³

et al. 2019

Preprint

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Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

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“…On top of these learning methods are the deep learning methods [26,27]. There are many different models of deep learning that have been introduced, such as stacked auto-encoder (SAE), deep belief nets (DBN), convolutional neural networks (CNNs), and Deep Boltzmann Machines (DBM) [28][29][30][31]. The superiority of the deep learning models in terms of accuracy has been established.…”

Section: Introductionmentioning

confidence: 99%

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

112

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The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

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Lung nodule segmentation and detection in computed tomography

Cited by 24 publications

References 25 publications

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

Liver Tumor Segmentation in CT Scans Using Modified SegNet

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