Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features

2017 ◽  
Author(s):  
Telagarapu Prabhakar ◽  
S. Poonguzhali
Keyword(s):  
Automatic Detection ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Malignant Lesions

scholarly journals AI Auxiliary Labeling and Classification of Breast Ultrasound Images

2021 ◽  
Vol 9 (2) ◽  
pp. 45-49
Author(s):  
Lei Wang ◽  
◽  
Biao Liu ◽  
Shaohua Xu ◽  
Ji Pan ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images

scholarly journals Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

2019 ◽  
Vol 38 (3) ◽  
pp. 762-774 ◽  
Author(s):  
Seung Yeon Shin ◽  
Soochahn Lee ◽  
Il Dong Yun ◽  
Sun Mi Kim ◽  
Kyoung Mu Lee
Keyword(s):  
Deep Learning ◽  
Breast Ultrasound ◽  
Ultrasound Images

Classification of breast ultrasound images using fractal feature

2005 ◽  
Vol 29 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Dar-Ren Chen ◽  
Ruey-Feng Chang ◽  
Chii-Jen Chen ◽  
Ming-Feng Ho ◽  
Shou-Jen Kuo ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images

scholarly journals Robust phase-based texture descriptor for classification of breast ultrasound images

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Lingyun Cai ◽  
Xin Wang ◽  
Yuanyuan Wang ◽  
Yi Guo ◽  
Jinhua Yu ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Texture Descriptor

scholarly journals Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1859
Author(s):  
Elham Yousef Kalafi ◽  
Ata Jodeiri ◽  
Seyed Kamaledin Setarehdan ◽  
Ng Wei Lin ◽  
Kartini Rahmat ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Loss Function ◽  
Low Cost ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Deep Convolutional Neural Networks ◽  
Hyperbolic Cosine ◽  
Reliable Classification ◽  
Network Approaches

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.

Computerized Classification of Mammary Gland Patterns in Whole Breast Ultrasound Images

2008 ◽  
pp. 188-195 ◽  
Author(s):  
Yuji Ikedo ◽  
Takako Morita ◽  
Daisuke Fukuoka ◽  
Takeshi Hara ◽  
Hiroshi Fujita ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Breast Ultrasound ◽  
Ultrasound Images
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