Automatic detection of lung cancers in chest CT images by variable N-Quoit filter

Okumura, Toshiaki; Miwa, Taeko; Kako, Jun-ichi; Yamamoto, Shozo; Matsumoto, Rie; Tateno, Yukio; Iinuma, Takeshi; Matsumoto, Tohru

doi:10.1109/icpr.1998.712041

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…Automatic lung cancer screening systems classify nodules using specific algorithms to extract nodule morphologii ical characteristics. Okumura et al, [29] distinguished solid nodules by using a Quoit filter that could detect only isolated nodules. In the case of isolated nodules, the graph of the pixel values becomes 'sharp,' and the nodule is detected when the annular filter passes through the graph.…”

Section: Volumetric Contextual Informationmentioning

confidence: 99%

“…In earlier work, researchers had mostly focused on extracting discriminative morphological features with the help of pathological knowledge about nodule types and applied relatively simple linear classifiers such as logistic regression or support vector machine [20,29,46]. Recently, with the surge of popularity and success in Deep Neural Networks (DNNs), which can learn hierarchical feature representations and class discrimination in a single framework, a myriad of DNNs has been proposed for medical image analysis [42,7,9,13,36,16].…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

et al. 2019

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Lung cancer is a global and dangerous disease, and its early detection is crucial to reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-toend manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from https://github.com/ku-milab/MGICNN .

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Section: Volumetric Contextual Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

et al. 2019

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“…The detailed architectures of the backbone network are shown in Table 1. The 3D feature pyramid network produces a total of four feature maps according to the (z, y, x) order: (64, 32, 32), (32,16,16), (16,8,8), and (8,4,4). Feature maps of different levels represent the detection capabilities of lesions with different sizes.…”

Section: Architecture Of the Networkmentioning

confidence: 99%

“…For the traditional methods, filters with different structures were used a lot to detect GGO nodules. Okumura et al 4 combined variable N‐Quoit filter (VNQ) filter with the point filter and the ring filter to extract the lesions in the lung. Compared with filters in past studies, the size of the filter could be adjusted adaptively based on the input image.…”

Section: Introductionmentioning

confidence: 99%

Automatic pulmonary ground‐glass opacity nodules detection and classification based on 3D neural network

Guo

Zhao

et al. 2022

Medical Physics

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Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantly essential. Methods: In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pretrained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask region-based convolutional neural networks (RCNN) to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracy further. Results:The experiments were conducted based on fivefold cross-validation with the imbalanced data set. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the competition performance metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposed method. Conclusions: We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses.

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“…A novel optimized set of features determined for both clustering and classification for detecting lung nodules on chest radiographs is presented in [6]. The 'N-Quoit' filter is studied for CAD of lung nodules on chest radiographs in [7]. A set of classification approaches are studied and compared for the detection of lung nodules in [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Transfer-to-Transfer Learning Approach for Computer Aided Detection of COVID-19 in Chest Radiographs

Narayanan

Hardie

Krishnaraja³

et al. 2020

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The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and apply transfer learning between these. We demonstrate the efficacy of the method using well-established deep convolutional neural networks. Our proposed training mechanism is more robust to limited training data and class imbalance. We study the performance of our algorithm(s) based on 10-fold cross validation and two hold-out validation experiments to demonstrate its efficacy. We achieved an overall sensitivity of 0.94 for the hold-out validation experiments containing 2265 and 2139 marked as COVID-19 chest radiographs, respectively. For the 10-fold cross validation experiment, we achieve an overall Area under the Receiver Operating Characteristic curve (AUC) value of 0.996 for COVID-19 detection. This paper serves as a proof-of-concept that an automated detection approach can be developed with a limited set of COVID-19 images, and in areas with scarcity of trained radiologists.

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Automatic detection of lung cancers in chest CT images by variable N-Quoit filter

Cited by 12 publications

References 3 publications

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

Automatic pulmonary ground‐glass opacity nodules detection and classification based on 3D neural network

Transfer-to-Transfer Learning Approach for Computer Aided Detection of COVID-19 in Chest Radiographs

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