3D tumor detection in automated breast ultrasound using deep convolutional neural network

2020 ◽  
Vol 47 (11) ◽  
pp. 5669-5680
Author(s):  
Yanfeng Li ◽  
Wen Wu ◽  
Houjin Chen ◽  
Lin Cheng ◽  
Shu Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Detection ◽  
Breast Ultrasound ◽  
Deep Convolutional Neural Network ◽  
Automated Breast Ultrasound

Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network

2020 ◽  
Vol 190 ◽  
pp. 105360 ◽  
Author(s):  
Woo Kyung Moon ◽  
Yao-Sian Huang ◽  
Chin-Hua Hsu ◽  
Ting-Yin Chang Chien ◽  
Jung Min Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Detection ◽  
Breast Ultrasound ◽  
Computer Aided ◽  
Automated Breast Ultrasound

3-D Res-CapsNet Convolutional Neural Network on Automated Breast Ultrasound Tumor Diagnosis

2021 ◽  
pp. 109608
Author(s):  
Ruey-Feng Chang ◽  
Huiling Xiang ◽  
Yao-Sian Huang ◽  
Chu-Hsuan Lee ◽  
Ting-Yin Chang Chien ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Automated Breast Ultrasound

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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