scholarly journals A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mengwan Wei ◽  
Yongzhao Du ◽  
Xiuming Wu ◽  
Qichen Su ◽  
Jianqing Zhu ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Morphological Features ◽  
High Dimensional ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

scholarly journals Fus2Net: A Novel Convolutional Neural Network for Classification of Benign and Malignant Breast Tumor in Ultrasound Images

2021 ◽  
Author(s):  
He Ma ◽  
Ronghui Tian ◽  
Hong Li ◽  
Hang Sun ◽  
Guoxiu Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Tumor ◽  
Rapid Development ◽  
Fine Tuning ◽  
Classification Model ◽  
Ultrasound Images ◽  
Malignant Breast ◽  
Auc Value

Abstract Background: The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high-efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images and makes rapid breast tumor screening possible. Results: The classification model was evaluated by using BUS tumor images without training. Evaluation indicators include accuracy, sensitivity, specificity, and Area Under Curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions: The experiment compared the existing CNN categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods: The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, 10-fold cross validation was employed. Meanwhile, to solve the balance of the dataset, the training data was augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image

2021 ◽  
Vol 11 (2) ◽  
pp. 424-431
Author(s):  
Yingxin Wang ◽  
Qianqian Zeng
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Texture Classification ◽  
Ultrasound Image ◽  
Texture Features ◽  
Recall Rate ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Feature Extraction Method

Texture analysis has always been active areas of ultrasound image processing research. Using texture features to classify the ultrasound images is the focus of researchers' attention. How to extract representative texture features is an important part of successful texture description. The research goal of this paper is to apply the deep neural network into the ultrasound classification of ovarian tumors, and design a novel type of ovarian cancer diagnosis system. The improved HOG feature extraction method and the gray-level concurrence matrix of LBP image are firstly adopted to extract low-level features; Then, these features are cascaded into a new feature vector, and are input into the auto-encoder neural network to learn the high-level feature. Finally, the SVM classifier is used to achieve the classification of ovarian lesion. A large number of qualitative and quantitative experiments show that the improved method has more performance than the comparisons algorithms for ovarian ultrasound lesion, and it can significantly improve the classification performance while ensuring the accuracy rate and recall rate.

scholarly journals Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
He Ma ◽  
Ronghui Tian ◽  
Hong Li ◽  
Hang Sun ◽  
Guoxiu Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Tumor ◽  
Rapid Development ◽  
Fine Tuning ◽  
Classification Model ◽  
Ultrasound Images ◽  
Malignant Breast ◽  
Auc Value

Abstract Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images

IRBM ◽  
2021 ◽  
Author(s):  
R. Karthik ◽  
R. Menaka ◽  
G.S. Kathiresan ◽  
M. Anirudh ◽  
M. Nagharjun
Keyword(s):  
Breast Tumor ◽  
Ultrasound Images

scholarly journals A case of giant cell tumor of the breast, clinically suspected as malignant breast tumor

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Mitsuo Terada ◽  
Naomi Gondo ◽  
Masataka Sawaki ◽  
Masaya Hattori ◽  
Akiyo Yoshimura ◽  
...  
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Breast Tumor ◽  
Cell Tumor ◽  
Malignant Breast Tumor ◽  
Malignant Breast

scholarly journals Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaofu Huang ◽  
Ming Chen ◽  
Peizhong Liu ◽  
Yongzhao Du
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Markov Random Fields ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
Texture Features ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Tumours ◽  
Data Set

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

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