Computer-Aided Detection of Lung Nodules with Fuzzy Min-Max Neural Network for False Positive Reduction

Zhai, Zhiwei; Shi, Daifeng; Cheng, Yuanzhi; Guo, Haoyan

doi:10.1109/ihmsc.2014.24

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Further, features including intensity and geometric features were extracted. The nodules were then classified using fuzzy min-max neural network classifier enhanced by K-means clustering [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Gopalakrishnan¹,

Kuppusamy

2014

Computational and Mathematical Methods in Medicine

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Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Gopalakrishnan¹,

Kuppusamy

2014

Computational and Mathematical Methods in Medicine

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“…Zhai et al [34] used adaptive border marching and placed developed rules into the section lung parenchyma and candidate nodules, after classifying eleven sorts of grey and geometric capabilities of candidate nodules, primarily based on a Fuzzy min-max neural community with the diagnostic sensitivity of 84%.…”

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confidence: 99%

An Effective Approach for Automated Lung Node Detection using CT Scans

Moragheb¹

2022

J Biomed Phys Eng

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Background: Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures.Objective: This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. Material and Methods:In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. Results:The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). Conclusion:Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.

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“…On the one hand, a lot of 2D data are processed in the early phase, like the 2D multiview convolutional neural network constructed by Setio et al, 1 which gets 85.4% detection rate at 1 FP/scan, the fuzzy min-max neural network used by Zhai et al 9 (84% sensitivity under 2.6 false positive per volume), and the off-the-shelf 2D CNN utilized by Bram et al 10 (0.71 average sensitivity with 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positive per subject). These dataoriented networks adaptively extract features of different chest medical images.…”

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confidence: 99%

Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection

Zhang

You

Luo

2018

Int J Imaging Syst Tech

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False positive reduction is a key procedure of computer-aided pulmonary nodule detection. The goal is to recognize the true pulmonary nodule from the plentiful candidates, which received from the first step of pulmonary nodule candidate detection. Convolutional networks can be used to perform false positive nodule reduction, but the classification accuracy need to be further improved. Recently, residual network is more and more popular around the world with its distinguished performance. A multicontext three-dimensional residual convolutional neural network (3D Res-CNN) was proposed to realize the reduction of the false positive nodule. Using two scales of network to adapt the variation of pulmonary nodule size, instead of using an unreferenced function with reference to the identity mapping, 3D Res-CNN uses a shortcut connection to realize the residual structure. For alleviating the data imbalance, firstly patches are rotated and resampled in original images; secondly weights are allotted for different labels in the calculation of cost function. Experiments on volumetric computed tomography (CT) data indicate that our method gets state of the art performance: 0.843 average sensitivity with 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positive per subject. The results show the effectiveness of residual convolutional network for the recognition of the true pulmonary nodule from the plentiful candidates. K E Y W O R D S computer aided diagnosis, deep learning, false positive reduction, pulmonary nodule detection, residual network

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Computer-Aided Detection of Lung Nodules with Fuzzy Min-Max Neural Network for False Positive Reduction

Cited by 10 publications

References 9 publications

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

An Effective Approach for Automated Lung Node Detection using CT Scans

Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection

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