TY - GEN
T1 - Deep Learning
T2 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
AU - Rahman, Shaikh Shiam
AU - Wimmer, Hayden
AU - Powell, Loreen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/6/20
Y1 - 2022/6/20
N2 - Deep learning has a large interest in medical image analysis as studies have shown several machine learning algorithms were successful in predicting disease. However, more work in needed to better understand the batch size, epoch, and learning rates. An empirical study of image processing with deep learning was conducted on the KIMIA path24 dataset. The rotation, width shifting, height shifting shear range, horizontal flip, and fill mode was used. The network was trained and validated by a total of 22,591 images from the KIMIA path24 dataset. ReLU was used for the convolution layer and softmax for the fully connected layer. Results found the batch size is inversely proportional to the network accuracy, the accuracy of a deep learning network is directly proportional to the number of epochs it passes through, and the learning rate does not bring any change to the network. The network performs best within a preferred learning rate.
AB - Deep learning has a large interest in medical image analysis as studies have shown several machine learning algorithms were successful in predicting disease. However, more work in needed to better understand the batch size, epoch, and learning rates. An empirical study of image processing with deep learning was conducted on the KIMIA path24 dataset. The rotation, width shifting, height shifting shear range, horizontal flip, and fill mode was used. The network was trained and validated by a total of 22,591 images from the KIMIA path24 dataset. ReLU was used for the convolution layer and softmax for the fully connected layer. Results found the batch size is inversely proportional to the network accuracy, the accuracy of a deep learning network is directly proportional to the number of epochs it passes through, and the learning rate does not bring any change to the network. The network performs best within a preferred learning rate.
KW - deep convolutional networks
KW - deep learning
KW - image classification
KW - machine learning
UR - https://www.scopus.com/pages/publications/85133873248
U2 - 10.1109/IEMTRONICS55184.2022.9795793
DO - 10.1109/IEMTRONICS55184.2022.9795793
M3 - Conference article
AN - SCOPUS:85133873248
SN - 9781665486842
T3 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
BT - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
A2 - Gill, Bob
A2 - Gangopadhyay, Malay
A2 - Poddar, Sanghamitra
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 June 2022 through 4 June 2022
ER -