Breast Ultrasound (BUS) Image Segmentation Using Regularized Rough <i>K</i>-Means (RRKM) Clustering: A Novel Approach

Saranya, P.; Samundeeswari, E. S.; Manavalan, R.

doi:10.1166/jmihi.2017.1989

Cited by 3 publications

(2 citation statements)

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“…Unsupervised learning algorithms can learn from unlabeled data sets and visually represent them. Unsupervised learning is primarily employed in two domains: first, it enables the extraction of representative features from high-dimensional data to reduce data sparsity and complexity through dimensionality reduction techniques, such as principal component analysis (PCA) ,,,,, and t-distributed stochastic neighbor embedding (t-SNE); ,− second, it facilitates grouping of data points on the basis of pairwise similarity metrics using clustering methods, like K-Means clustering ,− and hierarchical clustering, − thereby revealing inherent relationships among the data.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Lu,

Yang,

et al. 2024

ACS Sens.

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Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

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Section: Machine Learning Algorithmmentioning

confidence: 99%

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Lu,

Yang,

et al. 2024

ACS Sens.

View full text Add to dashboard Cite

show abstract

“…Cluster segmentation is a very important and widely used segmentation technique in the field of medical image segmentation. K‐means is one of the clustering methods [22, 23].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound image segmentation with multilevel threshold based on differential search algorithm

Shao

Xiang

et al. 2019

IET Image Processing

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Comparative Study Of Classification Techniques On Occurrence Of Breast Cancer Disease

Siddiqui

Ali

2019

2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)

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Breast Ultrasound (BUS) Image Segmentation Using Regularized Rough K-Means (RRKM) Clustering: A Novel Approach

Cited by 3 publications

References 0 publications

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Ultrasound image segmentation with multilevel threshold based on differential search algorithm

Comparative Study Of Classification Techniques On Occurrence Of Breast Cancer Disease

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