15th IEEE International Conference on Bioinformatics & Bioengineering (BIBE), 2015.
Abstract: Automated cell classification in Indirect Immunofluorescence (IIF) images has potential to be an important tool in clinical practice and research. This paper presents a framework for classification of Human Epithelial Type 2 cell IIF images using convolutional neural networks (CNNs). Previuos state-of-the-art methods show classification accuracy of 75.6% on a benchmark dataset. We conduct an exploration of different strategies for enhancing, augmenting and processing training data in a CNN framework for image classification. Our proposed strategy for training data and pre-training and fine tuning the CNN network led to a significant increase in the performance over other approaches that have been used until now. Specifically, our method achieves a 80.25% classification accuracy.
Download CNN Caffe Model and Source:
The trained caffe model used in the paper can be downloaded here: Download "Caffe Model"
Caffe proto file for training/testing: Download caffe proto file
Caffe solver file: Download solver file
Source files for data processing and augmentation (depends on the OpenCV library): Download source files
The network is shown below: