Performance comparison of various clustering techniques for diagnosis of breast cancer

Devi, R. Delshi Howsalya; Deepika, P.

doi:10.1109/iccic.2015.7435711

Cited by 10 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study presented the comparative study of clustering methods such as hierarchical clustering, farthest first, LVQ, canopy, and DBSCAN in Weka tool for the diagnosis of breast tumours. Accordingly to the presented result, it is concluded that the farthest first clustering technique had the highest prediction accuracy of 72% [10]. A study proposed deep classification algorithm for mammogram images.…”

Section: Related Work-supporting

confidence: 54%

Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Bandyopadhyay¹,

Dutta²

2020

Preprint

View full text Add to dashboard Cite

Breast Cancer diagnosis is one of the most studied problems in the medical domain. In the medical domain, cancer diagnosis has been studied extensively which instantiates the need of early prediction of cancer disease. For obtaining advance prediction, health records are exploited and given as input to an automated system. This paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining possibility of being affected by breast cancer. Proposed model is compared against other baseline classifiers such as stacked Simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that stacked GRU-LSTM-BRNN yield better classification performance for predictions related to breast cancer disease.

show abstract

Section: Related Work-supporting

confidence: 54%

Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Bandyopadhyay¹,

Dutta²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The study reported the most precise classification results, with an accuracy of 82.04 % [19]. In the Weka tool for the identification of BCs, a comparative analysis of clustering approaches, such as LVQ, hierarchical clustering, DBSCAN, and canopy, was undertaken in [20]. The first clustering technique was found to have the highest prediction accuracy of 72 % [20], according to the published results.…”

Section: Related Workmentioning

confidence: 98%

“…In the Weka tool for the identification of BCs, a comparative analysis of clustering approaches, such as LVQ, hierarchical clustering, DBSCAN, and canopy, was undertaken in [20]. The first clustering technique was found to have the highest prediction accuracy of 72 % [20], according to the published results. For BC classification, CNN and Multiple Instance Learning (MIL) were coupled.…”

Section: Related Workmentioning

confidence: 99%

Biomarker Based Detection and Staging of Breast Cancer from Blood Using Raman Spectroscopy and Deep Learning Technique

Selvarani

Jose

2023

Trends Sci

View full text Add to dashboard Cite

Breast cancer (BC) or breast neoplasm is causing major menace to the life of women around the world. The significance of early detection and staging of BC has been substantial in diagnosing protocol. This work aims to develop an automated system that combines multivariate data analysis (PCA - principal components analysis) with ensemble recurrent neural network models (stacked OGRU-LSTM) to identify Raman spectral characteristics that can be used as spectral cancer markers for the detection of BC progression and staging. Features of blood plasma from histopathologically diagnosed BC candidates were compared to healthy ones in this study. The same is performed on different leading classification models as the stacked basic RNN, the stacked-RNN-LSTM, and RNN-GRU models. A total of 2,340 Raman spectra generated is evaluated in this study. It is found from the study that stage 3 and stage 2 are structurally identical, but with PCA-Factorial Discriminant Analysis (FDA) they can be distinguished from each other, hence the Raman spectrum pertaining to blood plasma samples of the BC candidates is classified efficiently, yielding potentially high values of specificity and sensitivity for all the BC stages. Comparative classification results show that the stacked OGRU-LSTM model outperforms well for BC detection, and better differentiates various stages of BC by employing the multivariate data analysis technique. The stacked OGRU-LSTM model achieved the highest classification accuracy (97.89 %), Cohen-kappa score (0.928), F1-score (0.957), and the lowermost number of test loss and MSE (0.037), indicating that the model outperforms other baseline classifiers. HIGHLIGHTS The use of Raman spectroscopy in conjunction with deep learning models and multivariate data analysis to diagnose and categorize blood plasma samples as cancerous or noncancerous and staging of breast cancer based on their chemical composition To address the issue of underfitting and overfitting caused by insufficient Raman spectral data, spectral data augmentation techniques were implemented The potential for this technique is used to accurately classify breast cancerous samples and hence reduce the number of unnecessary excisional breast biopsies Stage 3 and stage 2 of breast cancer were found to be structurally identical but can be distinguished from each other using PCA-Factorial Discriminant Analysis with high specificity and sensitivity for all BC stages The stacked OGRU-LSTM model outperformed other baseline classifiers for breast cancer detection and better differentiated various stages of breast cancer by employing multivariate data analysis technique GRAPHICAL ABSTRACT

show abstract

“…Density-based spatial clustering of applications with noise (DBSCAN) is one of the most famous density-based clustering techniques. DBSCAN is more efficient in finding clusters, has the attribute of noise cancellation and is robust to outliers [31], [32]. The density-based algorithm is also broadly used on healthcare and medical datasets such as biomedical images.…”

Section: Related Workmentioning

confidence: 99%

Clustering algorithms for analysing electronic medical record: A mapping study

Shah

Rosli

2023

IJ-AI

View full text Add to dashboard Cite

Electronic Medical Records (EMRs) contain patients’ history related to their medication, vaccine, test results and insurance information. EMRs need to be stored to facilitate the application of clinical treatment and prevention protocols. Clustering algorithms automate the process of information extraction and support health data management. Hence, in this mapping study, we systematically examine the literature on clustering algorithms used for analysing EMRs. We focus on studies published in 2016-2021 to present an overview of clustering techniques used in these studies to analyse medical data. We found 27 studies on clustering techniques, clustering technique problems and the evaluation parameters for analysing EMRs. However, although several studies have focused on this topic, only a few have taken the significant step of examining the clustering techniques used for analysing medical data particularly electronic medical record. Our results highlight that three clustering techniques have been used to analyse medical data, namely, the partitioning, the hierarchical and the density-based algorithms. We identified several clustering technique problems and 10 different evaluation parameters. The results suggest that researchers should focus on analysing medical data that will drive data-driven decision-making by management and promote a data-driven culture to ensure health care quality.

show abstract

Performance comparison of various clustering techniques for diagnosis of breast cancer

Cited by 10 publications

References 5 publications

<strong>Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN</strong>

<strong>Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN</strong>

Biomarker Based Detection and Staging of Breast Cancer from Blood Using Raman Spectroscopy and Deep Learning Technique

Clustering algorithms for analysing electronic medical record: A mapping study

Contact Info

Product

Resources

About