TY - GEN
T1 - The Effect of Noisy and Blurry Data on Deep Learning
T2 - 2022 IEEE Region 10 International Conference, TENCON 2022
AU - Fadillah, Muhammad Fajar Azka
AU - Rumala, Dewinda Julianensi
AU - Purnomo, Mauridhi Hery
AU - Purnama, I. Ketut Eddy
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Convolutional Neural Networks (CNN) is one of the best Deep Learning algorithms commonly used for computer vision tasks, including medical image analysis. CNN can learn the representational features from images starting from the lower to complex features. However, noisy data can affect the generalization of the networks, which we can often find in medical images, such as Magnetic Resonance Imaging (MRI). In this paper, we intend to find the correlation between noisy data and the performance of CNN models. We build automatic CNN-based classifiers for normal brain MR images based on axial view by setting up three different data scenarios to train the classifiers: 1) original data, 2) blurred data, and 3) noisy data. We also evaluate the relationship between the prediction accuracy and kernel size of the convolutional layers. Based on our investigation, deeper layers and smaller kernels in the CNN models give better generalization.
AB - Convolutional Neural Networks (CNN) is one of the best Deep Learning algorithms commonly used for computer vision tasks, including medical image analysis. CNN can learn the representational features from images starting from the lower to complex features. However, noisy data can affect the generalization of the networks, which we can often find in medical images, such as Magnetic Resonance Imaging (MRI). In this paper, we intend to find the correlation between noisy data and the performance of CNN models. We build automatic CNN-based classifiers for normal brain MR images based on axial view by setting up three different data scenarios to train the classifiers: 1) original data, 2) blurred data, and 3) noisy data. We also evaluate the relationship between the prediction accuracy and kernel size of the convolutional layers. Based on our investigation, deeper layers and smaller kernels in the CNN models give better generalization.
KW - Blurry Data
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Magnetic Resonance Imaging Brain
KW - Noisy Data
KW - image classification
UR - http://www.scopus.com/inward/record.url?scp=85145651547&partnerID=8YFLogxK
U2 - 10.1109/TENCON55691.2022.9977591
DO - 10.1109/TENCON55691.2022.9977591
M3 - Conference contribution
AN - SCOPUS:85145651547
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
BT - Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 November 2022 through 4 November 2022
ER -