Breast tumors recognition based on edge feature extraction using support vector machine

Liu, Yangyang; Ren, Li; Xiao, Cao; Tong, Ying

doi:10.1016/j.bspc.2019.101825

Cited by 59 publications

(26 citation statements)

References 26 publications

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“…A previous study by Li et al (55) has applied four methods to classify benign and malignant breast tumors, and reported that the DT model achieved the best performance, with an AUC of 0.781, a sensitivity of 0.6, and a specificity of 0.894. Another study has used an SVM model for classifying benign and malignant breast tumors and obtained a sensitivity of 66.67% and a specificity of 93.55% (56). Wang et al (20) have used logistic regression analysis to distinguish benign and malignant breast tumors, and achieved an accuracy of 79.5%, a sensitivity of 0.607, a specificity of 0.800, and an AUC of 0.802.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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PurposeTo evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.Materials and MethodsA total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.ResultsIn the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808–0.906), 0.860 (95% CI, 0.806–0.904), 0.934 (95% CI, 0.891–0.963), and 0.921 (95% CI, 0.876–0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747–0.908), 0.784 (95% CI, 0.601–0.798), 0.890 (95% CI, 0.806–0.946), and 0.865 (95% CI, 0.777–0.928), respectively.ConclusionThe texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

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Section: Discussionmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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“…Similarly, a deep learning-based ultrasonic image classification, proposed by [ 22 ], used submodules with parameter selection to achieve a 96.41% accuracy. Another research study [ 31 ] used SVM, KNN, Discriminant Analysis, and random forest classifiers and achieved 82.69%, 63.49%, 78.85%, and 65.83% accuracy, respectively. The last research [ 43 ] in Table 9 used ensemble learning with CNNs such as DenseNet-X and VGG to identify breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…A morphological and edge-features analysis with a combinational approach is proposed, with a primary focus on the sum of curvatures based on histograms of their shapes. To classify with single morphological features and incorporate edge features, Support Vector Machine (SVM) is used [ 31 ]. Some previous works with their dataset details and results are given in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

et al. 2021

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Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

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“…Furthermore, MLbased techniques can provide judgment support to experts for an opportunity of the initial prognosis of breast tumors. Several machine learning techniques applied in the retrospective studies for the prediction of breast abnormalities, mass segmentation, and classification using pattern recognition [83], [84]. The most commonly used machine learning techniques discussed in this study are, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Fuzzy Method, Linear Discriminant Analysis (LDA),…”

Section: Machine Learning Models For Breast Lesions Diagnosticsmentioning

confidence: 99%

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Mahmood

Pei

et al. 2020

IEEE Access

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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

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Breast tumors recognition based on edge feature extraction using support vector machine

Cited by 59 publications

References 26 publications

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

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