Deep learning-based breast tumor detection and segmentation in 3D ultrasound image

2020 ◽  
Author(s):  
Yang Lei ◽  
Jincao Yao ◽  
Xiuxiu He ◽  
Dong Xu ◽  
Lijing Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
3D Ultrasound ◽  
Tumor Detection

A study on liver tumor detection from an ultrasound image using deep learning

2020 ◽  
Author(s):  
Takahiro Nakashima ◽  
Issei Tsutsumi ◽  
Hiroki Takami ◽  
Keisuke Doman ◽  
Yoshito Mekada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Tumor ◽  
Ultrasound Image ◽  
Tumor Detection

Breast Tumor Detection in Ultrasound Images Using Deep Learning

2017 ◽  
pp. 121-128 ◽  
Author(s):  
Zhantao Cao ◽  
Lixin Duan ◽  
Guowu Yang ◽  
Ting Yue ◽  
Qin Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Tumor ◽  
Tumor Detection ◽  
Ultrasound Images

scholarly journals Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
Shuxin Zhuang ◽  
Alex Noel Joseph Raj ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Deep Learning Features ◽  
Characteristic Features

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

Brain tumor detection and multi‐classification using advanced deep learning techniques

2021 ◽  
Author(s):  
Tariq Sadad ◽  
Amjad Rehman ◽  
Asim Munir ◽  
Tanzila Saba ◽  
Usman Tariq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Learning Techniques ◽  
Multi Classification

Numerical Simulation of Tunable Terahertz Graphene-Based Sensor for Breast Tumor Detection

2021 ◽  
Vol 21 (8) ◽  
pp. 9844-9851
Author(s):  
Aymen Hlali ◽  
Afef Oueslati ◽  
Hassen Zairi
Keyword(s):  
Numerical Simulation ◽  
Breast Tumor ◽  
Tumor Detection

3-D-Microwave Breast Tumor Detection: Study of System Performance

2008 ◽  
Vol 55 (12) ◽  
pp. 2772-2777 ◽  
Author(s):  
M.E. de Rodriguez ◽  
M. Vera-Isasa ◽  
V.S. del Rio
Keyword(s):  
Breast Tumor ◽  
System Performance ◽  
Tumor Detection
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