Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.
01 Jan 2020-Vol. 16, Iss: 6, pp 703-710
TL;DR: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM and found to be better than other discussed algorithms.
Abstract: BACKGROUND Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. OBJECTIVES The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. METHODS The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. RESULTS Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. CONCLUSION Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.
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TL;DR: Wang et al. as mentioned in this paper proposed a Multi-Tasking U-shaped Network (MT-UNet), which combines truncated normalization method and adaptive histogram equalization method to enhance the contrast of image.
Abstract: The benign and malignant (BM) classification of breast masses based on mammography is a key step in the diagnosis of early breast cancer and an effective way to improve the survival rate of patients. Nevertheless, due to the differences in size, shape and texture of breast masses and the visual similarity between masses of the same category, it is difficult to obtain a robust classification model using conventional deep learning methods. To address this problem, we proposed a Multi-Tasking U-shaped Network (MT-UNet), which contains three key ideas: 1) the U-shaped classification architecture constructed can well adapt to the heterogeneity of breast masses; 2) the combination of the proposed truncated normalization method and adaptive histogram equalization method can enhance the contrast of image; 3) training with label smoothing method can alleviate the problem of convergence difficulty caused by insufficient training data. The performance of the proposed scheme is evaluated on the public dataset of DDSM and INbreast. On the DDSM dataset, the Area Under Curve (AUC) and accuracy (ACC) reached 0.9963 and 0.9817, respectively. On the INbreast dataset, the AUC and ACC reached 0.9767 and 0.9391, respectively. Experimental results show that the proposed method can obtain a competitive performance.
8 citations
TL;DR: The diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy were estimated and the results should be interpreted with caution.
Abstract: In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85–0.90), 0.83 (95% CI: 0.78–0.87) and 0.84 (95% CI: 0.81–0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks’ linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.
6 citations
TL;DR: In this article , a deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.
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TL;DR: This paper shows that many kernel methods can be equivalently formulated as minimum enclosing ball (MEB) problems in computational geometry and obtains provably approximately optimal solutions with the idea of core sets, and proposes the proposed Core Vector Machine (CVM) algorithm, which can be used with nonlinear kernels and has a time complexity that is linear in m.
Abstract: Standard SVM training has O(m3) time and O(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. By observing that practical SVM implementations only approximate the optimal solution by an iterative strategy, we scale up kernel methods by exploiting such "approximateness" in this paper. We first show that many kernel methods can be equivalently formulated as minimum enclosing ball (MEB) problems in computational geometry. Then, by adopting an efficient approximate MEB algorithm, we obtain provably approximately optimal solutions with the idea of core sets. Our proposed Core Vector Machine (CVM) algorithm can be used with nonlinear kernels and has a time complexity that is linear in m and a space complexity that is independent of m. Experiments on large toy and real-world data sets demonstrate that the CVM is as accurate as existing SVM implementations, but is much faster and can handle much larger data sets than existing scale-up methods. For example, CVM with the Gaussian kernel produces superior results on the KDDCUP-99 intrusion detection data, which has about five million training patterns, in only 1.4 seconds on a 3.2GHz Pentium--4 PC.
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