Convolutional Neural Networks for Breast Tumor Classification using Structured Features

2021 ◽  
Author(s):  
Asrar Algarni ◽  
Bashayer A. Aldahri ◽  
Hanan S. Alghamdi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Breast Tumor Classification

scholarly journals Breast tumor classification in ultrasound images using support vector machines and neural networks

2016 ◽  
Vol 32 (3) ◽  
pp. 283-292 ◽  
Author(s):  
Carmina Dessana Lima Nascimento ◽  
Sérgio Deodoro de Souza Silva ◽  
Thales Araújo da Silva ◽  
Wagner Coelho de Albuquerque Pereira ◽  
Marly Guimarães Fernandes Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Support Vector ◽  
Ultrasound Images ◽  
Vector Machines ◽  
Breast Tumor Classification

Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks

2019 ◽  
Vol 50 (3) ◽  
pp. 2037-2052
Author(s):  
Enkh-Amgalan Boldbaatar ◽  
Lo-Yi Lin ◽  
Chih-Min Lin
Keyword(s):  
Neural Networks ◽  
Breast Tumor ◽  
Fast Convergence ◽  
Tumor Classification ◽  
Breast Tumor Classification

A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification

2018 ◽  
Vol 65 (9) ◽  
pp. 1935-1942 ◽  
Author(s):  
Yongjin Zhou ◽  
Jingxu Xu ◽  
Qiegen Liu ◽  
Cheng Li ◽  
Zaiyi Liu ◽  
...  
Keyword(s):  
Shear Wave ◽  
Breast Tumor ◽  
Shear Wave Elastography ◽  
Tumor Classification ◽  
Breast Tumor Classification

scholarly journals Incremental Dilations Using CNN for Brain Tumor Classification

2020 ◽  
Vol 10 (14) ◽  
pp. 4915 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Nishant Rodrigues ◽  
Y-h. Taguchi
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Classification Performance ◽  
Tumor Classification ◽  
Computational Overhead ◽  
Performance Accuracy ◽  
Artificial Brain ◽  
Dilation Rate

Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.

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