Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

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

doi:10.1016/j.artmed.2018.04.002

Cited by 70 publications

(38 citation statements)

References 32 publications

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“…The classification accuracy depends on different classifiers and their kernel types. An SVM is a supervised learning technique, but it can be applied to both classification and regression problems [33,34]. SVMs can generate optimal hyperplane in an iterative manner that maximizes the margin, where the margin is the largest distance to the nearest training data point of any class.…”

Section: Support Vector Machine (Svm) Classificationmentioning

confidence: 99%

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

et al. 2019

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An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature. Appl. Sci. 2019, 9, 2969 2 of 17 practitioners, for both diagnosis and treatment. Usually, the cancer detection process in histopathology consists of categorizing stained microscopic biopsy images into malignant and benign.The Gleason grade grouping system defines Gleason scores ≤ 6 as grade 1, score 3 + 4 = 7 as grade 2, score 4 + 3 = 7 as grade 3, score 4 + 4, 3 + 5 or 5 + 3 = 8 as grade 4, and score 4 + 5, 5 + 4 or 5 + 5 = 9 or 10 as grade 5. The Gleason score is obtained by adding the primary (most common) and secondary (second most common) scores from H&E stained tissue microscopic images. This system was developed by Dr. Donald F Gleason, who was a Pathologist in Minnesota, and members of the Veterans Administration Cooperative Urological Research Group (VACURG) [3]. This system was tested on a large number of patients, including long-term follow-ups and is considered an outstanding success.In recent years, an excellent and important addition to microscopy and digital imaging has been developed for microscopes that are used to convert stained tissue slides into whole slide digital images. This allows for more efficient computer-based viewing and analysis of histopathology. Early diagnosis and treatment are required, to avoid the enlargement of cancer cells in the prostate gland and control the spreading of more aggressive tumors to other parts of the body.The digital pathology field has grown dramatically over recent years, largely due to technological advancements in image processing and machine learning algorithms, and increases in computational power. As part of this field, many methods have been proposed for automatic histopathological image analysis and classification. In this paper, color segmentation, based...

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Section: Support Vector Machine (Svm) Classificationmentioning

confidence: 99%

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

et al. 2019

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“…Currently, most studies [48][49][50][51] have combined RFE with the SVM to perform feature selection.…”

Section: Two-sample T-testsmentioning

confidence: 99%

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

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“…Usually, the highly informative features are used to construct a classification model while disregarding the non-informative ones. By decreasing the number of features that are ultimately utilized for classification, there can be an enhancement in the performance of the algorithm [3,[8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

Rahman

Muniyandi

2020

Symmetry

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An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.

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Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

Cited by 70 publications

References 32 publications

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

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