Multi-view Convolutional Neural Network for lung nodule false positive reduction

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

doi:10.1016/j.eswa.2019.113017

Cited by 45 publications

(24 citation statements)

References 25 publications

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“…As a future work, we propose using a new deep learning model as an additional level to increase the localization accuracy of the tumor, and hence reduce the FN rate and increase the IoU metric, like the work introduced in [20].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, CNN models have been used widely in image classification for different applications [20,34,[40][41][42] or to extract features from the convolutional layers before or after the down sampling layers [41,43]. However, the architectures discussed above are not suitable for image segmentation or pixel-wise classifications.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Convolutional neural networks are similar to traditional neural networks [ 20,38,39]. A convolutional neural network (CNN) includes one or more layers of convolutional, fully connected, pooling, or fully connected and rectified linear unit (ReLU) layers.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Image segmentation is the process of partitioning the liver region in the CT scan into regions, where each region represents a semantic part of the liver [ 18,19]. This is a fundamental step to support the diagnosis by radiologists, and a fundamental step to create automatic computer-aided diagnosis (CAD) systems [20][21][22]. CT scans of the liver are usually interpreted by manual or semi-manual techniques, but these techniques are subjectiv e, expensive, time-consuming, and highly error prone.…”

Section: Introductionmentioning

confidence: 99%

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Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

Self Cite

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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Section: Discussionmentioning

confidence: 99%

Section: Basic Conceptsmentioning

confidence: 99%

Section: Basic Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

Self Cite

112

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“…Their research indicated that accurate lung segmentation is important in enhancing the efficiency of lung nodule detection systems. El-Regaily et al [10] used the rolling ball algorithm to reconstruct the lungs at the lung image segmentation stage by preserving the parts that are attached to the lung wall. , Their convolutional neural network was able to achieve an accuracy of 89.895%, which is better than most of the other models.…”

Section: A Background Removalmentioning

confidence: 99%

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

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Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image preprocessing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the "Rolling Ball Algorithm" and "Huang's Fuzzy Thresholding", which succeed in removing background from 100% of the images. For pectoral muscle removal "Canny Edge Detection" and "Hough's Line Transform" are used, which removed muscle from 99.06% of the images. "Invert", "CTI_RAS" and "ISOCONTOUR" lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

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Editorial for “Super‐Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging”

Gaga¹

2021

Magnetic Resonance Imaging

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Multi-view Convolutional Neural Network for lung nodule false positive reduction

Cited by 45 publications

References 25 publications

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Editorial for “Super‐Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging”

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