Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Albashish, Dheeb; Sahran, Shahnorbanun; Abdullah, Azizi; Shukor, Nordashima Abd; Pauzi, Suria Hayati Md

doi:10.18517/ijaseit.6.6.1489

Cited by 9 publications

(7 citation statements)

References 29 publications

(49 reference statements)

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“…Similarly, the testing was also performed based on a five-fold technique. This approach is useful for assessing model performance and identifying hyperparameters that enhance accuracy and reduce error [ 48 , 49 ]. The histological grades were classified as binary and multiclass to compare the performance of the AI techniques.…”

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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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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“…Although the state-of-the-art tissue structure-based CAD systems rely on the presence of tissue components, some basic tissue components, such as lumen, are occluded by cytoplasm (Nguyen, Sabata, & Jain, 2012a); consequently, accurate high-level features measurement cannot be acquired. In our previous researches, this limitation was circumvented by introducing an ensemble framework (Albashish et al, 2016), which was based on the texture features of the tissue components (mainly, lumen, cytoplasm, nuclei, and stroma). By using a dataset of 97 images at 40X magnification, this framework achieved 93.59% AUC performance for Grade 3 vs. Grade 4, which is the most challenging classification task in this domain.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the OVO and Ovall approaches (Galar, Fernández, Barrenechea, et al, 2011) discussed in the previous section, our latest published binary ensemble framework (Albashish et al, 2016) can be extended to address the three-class problem in PCa grading. Based on these reported approaches, two kinds of extensions for the ensemble framework can be introduced.…”

Section: Extension Of the Ensemble Framework To Multi-class Classificmentioning

confidence: 99%

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A Hierarchical Classifier for Multiclass Prostate Histopathology Image Gleason Grading

Albashish

Sahran

Abdullah

et al. 2018

JICT

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Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and oneversus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed. However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the 'divide

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“…Thus, the ensemble learning in machine learning is used to improve the diagnosis of the mammogram. It has shown its ability in solving different classification problems as histopathology image grading [16]- [18], intrusion detection system [19] and breast cancer detection and diagnosis [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Pruning based on Bee Algorithm for Mammogram Classification

Qasem

Abdullah

Sahran

et al. 2018

ijacsa

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In mammogram, masses are primary indication of breast cancer; and it is necessary to classify them as malignant or benign. In this classification task, Computer Aided Diagnostic (CAD) system by using ensemble learning is able to assist radiologists to have better diagnosis of mammogram images. Ensemble learning consists of two steps, generating multiple base classifiers and then combining them together. However, combining all base classifier in the ensemble model increases the computational cost and time. Therefore, ensemble pruning is an important step in ensemble learning to select the ensemble's members. Due to huge subsets of combination in the ensemble, selecting the proper ensemble subset is desirable. The objective of this paper is to select the optimal ensemble subset by using Bee Algorithm (BA). A pool of different classifier models such as Support vector machine, k-nearest neighbour and linear discriminant analysis classifiers have been trained using different samples of training data and 12 groups of features. Then, by using this pool of classifier models, BA was used to exploit and explore random uniform combination subsets of the trained classifiers. As a result, the best subset will be selected as the optimal ensemble. The mammogram image dataset that was used to test the model has been collected from Hospital Kuala Lumpur (HKL) and consists of 263 benign and malignant masses. The proposed method gives 77 % of Area Under Curve(AUC), 83% of accuracy, 93% of specificity and 60% of sensitivity.

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Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Cited by 9 publications

References 29 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

A Hierarchical Classifier for Multiclass Prostate Histopathology Image Gleason Grading

Heterogeneous Ensemble Pruning based on Bee Algorithm for Mammogram Classification

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