Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75368
Title: Deep learning techniques for biomedical image classification
Authors: Nguyen, Duc Long
Keywords: DRNTU::Engineering
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: Deep convolutional neural networks (deep CNNs) is currently the state-of-the-art methods for image classification. In biomedical image field, deep learning can be used with transfer learning to address the problem of small training datasets. For deep CNNs that are pretrained on large image datasets, their layers are general enough to be capable of generating useful features for an unseen biomedical dataset. In this research, we propose various deep CNNs based on transfer learning technique. We come out with a feature concatenation scheme that effectively exploit the power of deep neural networks. We also experiment new technique such as ensemble learning for accuracy improvement. The experiments on the 2D-Hela and the PAP-smear microscopic datasets show that our proposed structures have significant performance improvements over several traditional classification methods. Part of this project have been accepted as a poster session in the IEEE International Symposium on Circuits and Systems (ISCAS) 2018 conference.
URI: http://hdl.handle.net/10356/75368
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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