Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection

Nithya,; Bhuvaneswari,; Senthil,

doi:10.11648/j.ajnna.20190501.13

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Cluster analysis identifies nuclear patterns and community structure in the histological sections and identifies similar groups in datasets. Data are clustered based on their similarity [ 34 , 35 ]. The Euclidean distance measure used to compute the distance between two data points can be expressed as:

where

is the Euclidean distance,

are the centroid points, and

and

are the inter- and intra-cluster distances, respectively.…”

Section: Methodsmentioning

confidence: 99%

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

et al. 2021

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Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

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where

is the Euclidean distance,

are the centroid points, and

and

are the inter- and intra-cluster distances, respectively.…”

Section: Methodsmentioning

confidence: 99%

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

et al. 2021

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“…For predictive probabilities, the intuitive fuzzy c-means clustering identifies abnormalities in breast cancer patients' mammography images and symptoms. Pearson Chi-Square test (x2) at the level significance of 0.05 suggested a strong link between mammography performance and breast cancer clinical symptoms [14].…”

Section: Related Workmentioning

confidence: 99%

Robust Breast cancer Detection using Faster R-CNN Algoritma

Pratiwi

Sari

2022

ijicom

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One of the most common screening tools for breast cancer detection is ultrasound. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Faster R-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, conducting pre-processing, training models, and evaluating the model performance. Based on the experiment result, we obtain that this model can detect breast cancer with bounding boxes. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy.

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“…The MSTP is one of the most fundamental and well-known combinatorial optimization problems in the classical graph theory. The MSTP has many application areas, such as transportation [56], logistics [57], image processing [58], statistical cluster analysis [59], wireless telecommunication networks [60], and distribution systems [61]. In a classical deterministic case, we assumed that the values of all edge weights are crisp numbers.…”

Section: Application Of Nlpifns In the If Minimum Spanning Tree Problemmentioning

confidence: 99%

Defuzzification of Non-Linear Pentagonal Intuitionistic Fuzzy Numbers and Application in the Minimum Spanning Tree Problem

Mert

2023

Symmetry

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In recent years, with the variety of digital objects around us becoming a source of information, the fields of artificial intelligence (AI) and machine learning (ML) have experienced very rapid development. Processing and converting the information around us into data within the framework of the information processing theory is important, as AI and ML techniques need large amounts of reliable data in the training and validation stages. Even though information naturally contains uncertainty, information must still be modeled and converted into data without neglecting this uncertainty. Mathematical techniques, such as the fuzzy theory and the intuitionistic fuzzy theory, are used for this purpose. In the intuitionistic fuzzy theory, membership and non-membership functions are employed to describe intuitionistic fuzzy sets and intuitionistic fuzzy numbers (IFNs). IFNs are characterized by the mathematical statements of these two functions. A more general and inclusive definition of IFN is always a requirement in AI technologies, as the uncertainty introduced by various information sources needs to be transformed into similar IFNs without neglecting the variety of uncertainty. In this paper, we proposed a general and inclusive mathematical definition for IFN and called this IFN a non-linear pentagonal intuitionistic fuzzy number (NLPIFN), which allows its users to maintain variety in uncertainty. We know that AI technology implementations are performed in computerized environments, so we need to transform the IFN into a crisp number to make such IFNs available in such environments. Techniques used in transformation are called defuzzification methods. In this paper, we proposed a short-cut formula for the defuzzification of a NLPIFN using the intuitionistic fuzzy weighted averaging based on levels (IF-WABL) method. We also implemented our findings in the minimum spanning tree problem by taking weights as NLPIFNs to determine the uncertainty in the process more precisely.

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Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection

Cited by 3 publications

References 13 publications

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Robust Breast cancer Detection using Faster R-CNN Algoritma

Defuzzification of Non-Linear Pentagonal Intuitionistic Fuzzy Numbers and Application in the Minimum Spanning Tree Problem

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