Deep Learning: An Empirical Study on Kimia Path24
Document Type
Conference Proceeding
Publication Date
6-20-2022
Publication Title
IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) Proceedings
DOI
10.1109/IEMTRONICS55184.2022.9795793
Abstract
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.
Recommended Citation
Rahman, Shaikh Shiam, Hayden Wimmer, Loreen Marie Powell.
2022.
"Deep Learning: An Empirical Study on Kimia Path24."
IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) Proceedings: IEEE Xplore.
doi: 10.1109/IEMTRONICS55184.2022.9795793
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/172
Comments
Georgia Southern University faculty member, Hayden Wimmer co-authored Deep Learning: An Empirical Study on Kimia Path24.