Breast Cancer Detection Using K-Nearest Neighbors, Logistic Regression and Ensemble Learning

Rawat, Ram Murti; Panchal, Shivam; Singh, Vivek Kumar; Panchal, Yash

doi:10.1109/icesc48915.2020.9155783

Cited by 46 publications

(20 citation statements)

References 4 publications

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“…Support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost) had been commonly utilized in related studies of liver lesions, breast lesions, thymomas, and cardiac arrhythmia. 12,[26][27][28] The optimal hyperparameters were determined through a grid search for the 3 classifier methods in the Python scikit-learn environment (version 0.23.2). First, the search space for hyperparameters was created based on the preliminary experimental results.…”

Section: Classifier Methodsmentioning

confidence: 99%

“…RFE was used by many studies as a feature selection method because of its stability and effectiveness, 19,[21][22][23] it recursively trimmed the feature set according to current importance until it contained the required number of features. RF and chisquare tests had exhibited efficacy in some studies, [24][25][26][27] the former utilized the out-of-bag error of each decision tree to calculate the importance of each feature and sorted them in order of importance; the later used the correlation between the label and each feature to obtain the chisquare value of each feature and then sorted these values from large to small. Three feature selections were implemented in the Python scikit-learn environment (version 0.23.2).…”

Section: Radiomics Features Selectionmentioning

confidence: 99%

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An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions

Wang

Yang

et al. 2021

J of Ultrasound Medicine

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Objectives To develop and test an optimized radiomics model based on multi‐planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions. Methods Patients (n = 200) with breast lesions who underwent ABVS examinations were included. For each patient, 208 radiomics features were extracted from the ABVS images, including axial plane and coronal plane. Recursive feature elimination, random forest, and chi‐square test were used to select features. A support vector machine, logistic regression, and extreme gradient boosting were utilized as classifiers to differentiate malignant and benign breast lesions. The area under the curve, sensitivity, specificity, accuracy, and precision was used to evaluate the performance of the radiomics models. Generalization of the radiomics models was verified through 5‐fold cross‐validation. Results For a single plane or a combination of planes, a combination of recursive feature elimination, and support vector machine yielded the best performance when identifying breast lesions. The machine learning models based on a combination of planes performed better than those based on a single plane. Regarding the axial plane and coronal plane, the machine learning model using a combination of recursive feature elimination and support vector machine yielded the optimal identification performance: average area under the curve (0.857 ± 0.058, 95% confidence interval, 0.763–0.957); the average values of sensitivity, specificity, accuracy, and precision were 87.9, 68.2, 80.7, and 82.9%, respectively. Conclusions The optimized radiomics model based on ABVS images can provide valuable information for identifying benign and malignant breast lesions preoperatively and guide the accurate clinical treatment. Further external validation is required.

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Section: Classifier Methodsmentioning

confidence: 99%

Section: Radiomics Features Selectionmentioning

confidence: 99%

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions

Wang

Yang

et al. 2021

J of Ultrasound Medicine

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“…Features are extracted using PCA and transferred to the retraining phase. The model uses a small amount of data and achieves better accuracy and the accuracy could become better by using a large amount of data [49]. [50] presented a knowledge-based model to classify the cancer.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

et al. 2021

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Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging. In this study, a novel patchbased deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN). Features are extracted through an unsupervised pre-training and supervised fine-tuning phase. The network automatically extracts features from image patches. Logistic regression is used to classify the patches from histopathology images. The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background). The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86%. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods.

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“…In essence, logistic regression is a regression model that predicts that a given data item or entry is likely to belong to a given class using the regression model. Logistic regression uses a sigmoid function to model the data, as illustrated in Figure 7 [63,64,65]. Logistic regression has many important points, such as implementation simplicity, computational effectiveness, training-based effectiveness, ease of regularization.…”

Section: G Logistic Regressionmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

155

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Breast Cancer Detection Using K-Nearest Neighbors, Logistic Regression and Ensemble Learning

Cited by 46 publications

References 4 publications

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions

An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

The Role of Machine Learning Algorithms for Diagnosing Diseases

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