Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Ye, Yuyun; Tian, Miao; Liu, Qiyu; Tai, Heng–Ming

doi:10.1109/access.2020.3026168

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…Anova test is performed to supplement the P-Values, which records the significant difference between errors of the proposed and existing method in Table 12. Take P-Value = 0.05 as the evaluation standard of method difference, it is obviously noted that our woks are significantly different from the works in [10], [11], [41], [42] and [44] but equivalent to the rest works with high CPM.…”

Section: Discussionmentioning

confidence: 94%

Pulmonary Nodule Detection Using 3-D Residual U-Net Oriented Context-Guided Attention and Multi-Branch Classification Network

Yuan

Cheng

et al. 2022

IEEE Access

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Accurate detection of pulmonary nodules on chest computed tomography scans is crucial to early diagnosis of lung cancer. To address the thorn problems on low detection sensitivity and high falsepositive rate caused by heterogeneity and morphological complexity of 3-D nodule features, a computeraided detection system is developed to increase the detection sensitivity and classification accuracy of pulmonary nodules. The contributions include: (1) Nodule candidate detection: 3-D Residual U-Net model is improved to detect candidate nodules, which constructs 3-D context-guided module to extract local and global nodule features by setting the dilated convolution with different dilation rates. Furthermore, channel attention mechanism is used to dynamically adjust the channel features, which enhances the generalization and expression ability of the detection-network to effectively learn 3-D spatial context features. (2) False-positive reduction: multi-branch classification network is designed for multi-task learning. Image reconstruction task is performed to retain more microscopic nodules information from convolutional neural network (CNN) hierarchy. Moreover, each branch deals with the feature map at corresponding depth layers, and then all branches' feature maps are combined together to perform nodule classification task. Numerous experimental results show that the proposed system is perfectly qualified for pulmonary nodules detection on Lung Nodules Analysis 2016 dataset, which achieves detection sensitivity up to 94.0% and competition performance metric (CPM) score up to 0.959.

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Section: Discussionmentioning

confidence: 94%

Pulmonary Nodule Detection Using 3-D Residual U-Net Oriented Context-Guided Attention and Multi-Branch Classification Network

Yuan

Cheng

et al. 2022

IEEE Access

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“…Other traditional vision algorithms found successful results in juxtapleural nodules detection [ 89 ]. In the context of this problem, missing a true nodule should be more penalized than predicting too many false suspicions; however, there is an obvious effort in the literature to decrease false positive mistakes, mostly approached by combining different classification networks [ 78 , 90 ], using multi-scaled patches for capturing features at different expression levels [ 80 , 81 , 91 , 92 ], employing other classification algorithms, such as SVM [ 82 , 86 , 87 , 93 , 94 , 95 ], Bayesian networks, and neuro-fuzzy classifiers [ 95 ], or proposing a graph-based image representation with deep point cloud models [ 96 ].…”

Section: Computer-aided Decision Systemsmentioning

confidence: 99%

“…The heterogeneity and high variability of nodule imaging characteristics bring significant complexity into this task, and so lung nodule detection can naturally be seen separated in two sub-modules: (1) where multiple candidates are first proposed, and (2) the nodule/non-nodule distinction is refined. Considering DL-based approaches, encoder-decoder architectures are widely used as the base methods for initial nodule detection [78][79][80][81][82][83][84][85]. The extraction of hand-crafted statistical, shape, and texture features also brought valuable information for candidate detection, being further classified by SVM [86,87] or by using ensemble strategies to combine the learning abilities of different classifiers [88].…”

Section: Nodule Detectionmentioning

confidence: 99%

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Silva

Pereira

Neves

et al. 2022

JPM

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Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

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“…A hard mining strategy obtains a false-positive reduction efficiency. Yuyun Ye et al [43] implemented V-Net and High-Level Descriptors based on SVM classifiers to enhance FP reduction. The novelty of SVM is that it includes a wavelet feature.…”

Section: Classifiermentioning

confidence: 99%

Detection and Classification of Neonatal Jaundice Using Color Card Techniques – A Study

2022

Int. J. Onl. Eng.

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--- In a newborn baby, the particular complication is jaundice. Jaundice is due to many reasons. An increase in the bilirubin level denotes the presence of jaundice. Neonatal hyperbilirubinemia is most common in the first postnatal week. Early prediction and classification of jaundice are essential to reduce the mortality rate. Diagnosis of the disease using computer vision techniques helps to achieve accuracy in prediction and classification. Here I studied and extracted the information from medical data mining and achieved the goal. It involves examining, analyzing, extracting, selecting, and classifying the features. Finally, it provides treatment suggestions It enables the physician to perform early prediction and provide appropriate jaundice treatment. Computer vision and machine learning techniques make the process easy and simple. The optimized method of classification increases accuracy.

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Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Cited by 20 publications

References 36 publications

Pulmonary Nodule Detection Using 3-D Residual U-Net Oriented Context-Guided Attention and Multi-Branch Classification Network

Pulmonary Nodule Detection Using 3-D Residual U-Net Oriented Context-Guided Attention and Multi-Branch Classification Network

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Detection and Classification of Neonatal Jaundice Using Color Card Techniques – A Study

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