Comparing random forest and support vector machines for breast cancer classification

Aroef, C.; Rivan, Yuda; Rustam, Zuherman

doi:10.12928/telkomnika.v18i2.14785

Cited by 51 publications

(26 citation statements)

References 24 publications

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“…Notwithstanding variations in the experimental outcomes, the SVM is equated to more traditional models in many of these studies. In contrast, other studies [13], [14], [16], [19] reported that the SVM grippingly outperformed several conventional models.…”

Section: Introductionmentioning

confidence: 76%

“…The SVM employs the risk minimisation theory to establish the best separation hyperplane in multi-dimensional space to classify a bipartite outcome [11]. Initially, the SVM was designed for binary classification [12]; however, of late, the SVM is applicable for both classification and The performance of the SVM has been compared with other ML algorithms, such as Bayesian logistic regression, and decision tree [13], [14], random forest [15], [16], neural network [17], [18] and k-nearest neighbours [19], [20]. Notwithstanding variations in the experimental outcomes, the SVM is equated to more traditional models in many of these studies.…”

Section: Introductionmentioning

confidence: 99%

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An empirical assessment of different kernel functions on the performance of support vector machines

et al. 2021

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Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.

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Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

An empirical assessment of different kernel functions on the performance of support vector machines

et al. 2021

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“…We can use any classifier since it is a two-class problem; we used SVM with different kernels. The design of SVM is based on a large margin theory and gives very good results in many binary classification problems [ 43 , 44 , 45 , 46 ]. The SVM model is trained using the predictions of the training and the validation datasets.…”

Section: Proposed Methodsmentioning

confidence: 99%

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

et al. 2021

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Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.

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“…Breast cancer is a condition in which cells grow out of control, resulting in a tumour that can spread throughout the body. Although the specific causes of breast cancer are unknown, researchers believe that aberrant cell growth is caused by a combination of genes, lifestyle, environment, and hormones [2]. This breast cancer must be detected in its early stage otherwise it may cause death.…”

Section: Introductionmentioning

confidence: 99%

Automated breast cancer detection system from breast mammogram using deep neural network

Chittineni¹,

Edara²

2022

IJEECS

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<p>All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.</p>

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Comparing random forest and support vector machines for breast cancer classification

Cited by 51 publications

References 24 publications

An empirical assessment of different kernel functions on the performance of support vector machines

An empirical assessment of different kernel functions on the performance of support vector machines

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

Automated breast cancer detection system from breast mammogram using deep neural network

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