Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells

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.