Publication Type : Conference Proceedings
Publisher : 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), IEEE
Source : 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), IEEE, Bangalore, India, p.1-6 (2017)
Url : https://ieeexplore.ieee.org/abstract/document/8593186
Keywords : Biomechanics, cytopathological testing, Deep learning, deep neural architectures, Diseases, Ensemble learning, Ensemble Methods, Feature extraction, fine-tuned features, image classification, image cytometry, Image segmentation, learning (artificial intelligence), Leukemia cell classification, Medical Image Processing, Microfluidics, microfluidics-based imaging flow cytometry, microscopy, mIFC, modified deep neural network, neural net architecture, Neural networks, shallow network embedding, substantial improvement, Training, Transfer learning, Tuning
Campus : Amritapuri
School : School of Computing, Department of Computer Science and Engineering, School of Engineering
Center : Computer Vision and Robotics, Center for Computational Engineering and Networking, Research & Projects
Department : Center for Computational Engineering and Networking (CEN), Computer Science
Verified : Yes
Year : 2017
Abstract : One of the most crucial parts in the diagnosis of a wide variety of ailments is cytopathological testing. This process is often laborious, time consuming and requires skill. These constraints have led to interests in automating the process. Several deep learning based methods have been proposed in this domain to enable machines to gain human expertise. In this paper, we investigate the effectiveness of transfer learning using fine-tuned features from modified deep neural architectures and certain ensemble learning methods for classifying the leukemia cell lines HL60, MOLT, and K562. Microfluidics-based imaging flow cytometry (mIFC) is used for obtaining the images instead of image cytometry. This is because mIFC guarantees significantly higher throughput and is easy to set up with minimal expenses. We find that the use of fine-tuned features from a modified deep neural network for transfer learning provides a substantial improvement in performance compared to earlier works. We also identify that without any fine tuning, feature selection using ensemble methods on the deep features also provide comparable performance on the considered Leukemia cell classification problem. These results show that automated methods can in fact be a valuable guide in cytopathological testing especially in resource limited settings.
Cite this Research Publication : K. S. Kalmady, Kamath, A. S., Gopakumar G, Subrahmanyam, G. R. K. S., and Gorthi, S. S., “Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells”, 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, Bangalore, India, pp. 1-6, 2017