Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions

Netto, Stelmo Magalhães Barros; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Nunes, Rofolfo Acatauassu; Gattass, Marcelo

doi:10.1109/tip.2018.2878954

Cited by 11 publications

(2 citation statements)

References 34 publications

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“…To explain the nodule appearance without ignoring spatial information, a 7th Order Markov Gibbs Random Field and a Local Binary Pattern are devised by Shaffie et al [24]. Netto et al [25] proposed a methodology for analyzing lung lesions using temporal evaluation, which can aid in the diagnosis of indeterminate lesions during treatment. The modified quality threshold clustering technique was employed to assign each voxel of the lesion to a cluster, and the alteration in the lesion was evaluated by analyzing the movement of voxels to other clusters over time.…”

Section: Plos Onementioning

confidence: 99%

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Majumder,

Gautam,

Basu

et al. 2024

PLoS ONE

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Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.

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Section: Plos Onementioning

confidence: 99%

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Majumder,

Gautam,

Basu

et al. 2024

PLoS ONE

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show abstract

“…When the threshold value is not given, BIRCH uses the computer's memory to estimate the threshold value, following which the existing clustering methods are used as the global clustering method. In some real-world problems, a threshold value is often given as a criterion of clustering [11]- [17]. This criterion is similar to t in a distance-t stopping condition [18], where two clusters within t are grouped into the same cluster.…”

Section: Introductionmentioning

confidence: 99%

Mapreduce-Based Distributed Clustering Method Using CF⁺ Tree

Ryu

Jung

2020

IEEE Access

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Clustering exceptionally large data sets is becoming a major challenge in data analytics with the continuous increase in their size. Summary-based clustering methods and distributed computing frameworks such as MapReduce can efficiently handle this challenge. These methods include BIRCH and its extension CF +-ERC. CF +-ERC can reduce the clustering time of large data sets by utilizing the structure of a CF + tree. However, CF +-ERC is a sequential clustering method, so it cannot be used with multiple machines to reduce the clustering time. In this study, we propose a novel MapReduce-based distributed clustering method called CF +-ERC on MapReduce (CF + ERC_MR). It builds a CF + tree for clustering an exceptionally large data set with a given threshold and finds the final clusters using MapReduce, which significantly reduces the clustering time. Further, our method is scalable with respect to the number of machines. The efficacy of this method is validated through not only its theoretical analysis but also indepth experimental analysis of exceptionally large synthetic and real data sets. The experimental results demonstrate that the clustering speed of our approach is far superior to that of the existing clustering methods.

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Multi-view clustering via neighbor domain correlation learning

Zhou

et al. 2020

Neural Comput & Applic

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Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions

Cited by 11 publications

References 34 publications

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Mapreduce-Based Distributed Clustering Method Using CF⁺ Tree

Multi-view clustering via neighbor domain correlation learning

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Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions

Cited by 11 publications

References 34 publications

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Mapreduce-Based Distributed Clustering Method Using CF+ Tree

Multi-view clustering via neighbor domain correlation learning

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Mapreduce-Based Distributed Clustering Method Using CF⁺ Tree