Automatic Sub Classification of Benign Breast Tumor

2017 ◽  
pp. 221-232
Author(s):  
Aparna Bhale ◽  
Manish Joshi
Keyword(s):  
Breast Tumor ◽  
Benign Breast Tumor

Capillary Hemangioma of the Breast-a Rare Type of benign Breast Tumor

2015 ◽  
Vol 22 (3) ◽  
pp. 355-356 ◽  
Author(s):  
Wei Ma ◽  
Feng Jin ◽  
Yunfei Wu
Keyword(s):  
Breast Tumor ◽  
Capillary Hemangioma ◽  
Rare Type ◽  
Benign Breast Tumor

Serum Phospholipid Fatty Acids in Benign Breast Tumor and Breast Cancer

2009 ◽  
Vol 42 (3) ◽  
pp. 213
Author(s):  
Eugene Shim ◽  
Sei Hyun Ahn ◽  
You-Jeong Hwang ◽  
Yang Cha Lee-Kim
Keyword(s):  
Breast Cancer ◽  
Fatty Acids ◽  
Breast Tumor ◽  
Phospholipid Fatty Acids ◽  
Serum Phospholipid ◽  
Benign Breast Tumor

scholarly journals Excision of benign breast tumor by an Ultrasound-Guided hand held Mammotome biopsy device

2005 ◽  
Vol 8 (3) ◽  
pp. 92 ◽  
Author(s):  
Hai Lin Park ◽  
Jin Young Kwak ◽  
Seung Hee Lee ◽  
Hae Kyoung Jung ◽  
Ji Young Kim ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Ultrasound Guided ◽  
Benign Breast Tumor ◽  
Biopsy Device ◽  
Mammotome Biopsy

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 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.

Breast Tumor Classification of Ultrasound Images Using a Reversible Round-Off Nonrecursive 1-D Discrete Periodic Wavelet Transform

2009 ◽  
Vol 56 (3) ◽  
pp. 880-884 ◽  
Author(s):  
Hsieh-Wei Lee ◽  
Bin-Da Liu ◽  
King-Chu Hung ◽  
Sheau-Fang Lei ◽  
Chin-Feng Tsai ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Periodic Wavelet ◽  
Breast Tumor Classification

Serum VEGF levels in women with a benign breast tumor or breast cancer

1999 ◽  
Vol 53 (2) ◽  
pp. 161-166 ◽  
Author(s):  
Petri Salven ◽  
Vesa Perhoniemi ◽  
Heikki Tykkä ◽  
Hanna Mäenpää ◽  
Heikki Joensuu
Keyword(s):  
Breast Cancer ◽  
Breast Tumor ◽  
Benign Breast Tumor

scholarly journals Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 4835-4841
Keyword(s):  
Transfer Learning ◽  
Early Identification ◽  
Breast Tumor ◽  
Early Detection Of Cancer ◽  
Medical Images ◽  
Term Survival ◽  
Learning Framework ◽  
Mammographic Images

Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.

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