TY - GEN
T1 - Subtype Classification of Renal Parenchymal Tumors on MLP-Based Methods
AU - Hao, Shang Ben
AU - Wang, Shuai
AU - Du, Hui Qian
AU - Chen, Yan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Renal parenchymal tumors are among the most common tumors in humans. With the development of deep learning, it has become possible to use deep neural networks to distinguish renal parenchymal tumor subtypes. This paper aims to investigate the role of the Multilayer Perceptron (MLP) structure in the classification of renal parenchymal tumor subtypes on magnetic resonance (MR) images. We design a classification model based on ConvMLP. In addition, we introduce Convolutional Block Attention Modules (CBAMs) on the basis of ConvMLP to further improve the classification precision. In order to find where adding CBAMs improves the performance the most, we design four variant networks. We conduct extensive comparative experiments on these four variant networks and other convolutional neural networks. The experimental results show that the addition of CBAM improves the classification precision of renal parenchymal tumor subtypes by 3%, and compared with other CNNs, our classifier has the highest precision.
AB - Renal parenchymal tumors are among the most common tumors in humans. With the development of deep learning, it has become possible to use deep neural networks to distinguish renal parenchymal tumor subtypes. This paper aims to investigate the role of the Multilayer Perceptron (MLP) structure in the classification of renal parenchymal tumor subtypes on magnetic resonance (MR) images. We design a classification model based on ConvMLP. In addition, we introduce Convolutional Block Attention Modules (CBAMs) on the basis of ConvMLP to further improve the classification precision. In order to find where adding CBAMs improves the performance the most, we design four variant networks. We conduct extensive comparative experiments on these four variant networks and other convolutional neural networks. The experimental results show that the addition of CBAM improves the classification precision of renal parenchymal tumor subtypes by 3%, and compared with other CNNs, our classifier has the highest precision.
KW - Convolutional Block Attention Modules
KW - MLP
KW - Magnetic resonance imaging
KW - Renal parenchymal tumor
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=85136966802&partnerID=8YFLogxK
U2 - 10.1109/CTISC54888.2022.9849736
DO - 10.1109/CTISC54888.2022.9849736
M3 - Conference contribution
AN - SCOPUS:85136966802
T3 - CTISC 2022 - 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications
BT - CTISC 2022 - 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications
A2 - Gerogianni, Vassilis C.
A2 - Yue, Yong
A2 - Kamareddine, Fairouz
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022
Y2 - 22 April 2022 through 24 April 2022
ER -