Artificial Intelligence-Based Medical Image Classification using a Multilayer Fuzzy Approach
Author:
Affiliation:
1. Statistics eTeachers Group, Royal Statistical Society 2110, 100 Leeward Glenway, Toronto, Ontario, M3C 2Z1, CANADA
2. School of Computing, University of Portsmouth, Winston Churchill Ave, South Sea, Portsmouth PO1 2UP Portsmouth, UNITED KINGDOM
Abstract
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
General Computer Science
Reference10 articles.
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2. Ciresan, Dan; Ueli Meier; Jonathan Masci; Luca M. Gambardella; Jurgen Schmidhuber, Flexible, High-Performance Convolutional Neural Networks for Image Classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Vol. 2, pp.1237–1242.
3. Liu S., Deng W., “Very Deep Convolutional Neural Network Based Image Classification Using Small Training Sample Size”, 3rd IAPR Asian Conference on Pattern Recognition, 2015. https://doi.org/10.1109/ACPR.2015.7486599
4. Zhao P., Li C., Rahaman M. M., Xu H., Yang H., Sun H., Jiang T., Grzegorzek M., A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers, Systems Microbiology, Vol. 13, 2022. https://doi.org/10.3389/fmicb.2022.792166.
5. Phung V. H., Rhee E. J.. A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets, Applied Sciences, 9(21), 4500, 2019.
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