Abstract
Red Blood Cells (RBCs) play an important role in the welfare of human being as it helps to transport oxygen throughout the body. Different RBC-related diseases, for example, variants of anemias, can disrupt regular functionality and become life-threatening. Classification systems leveraging CNNs can be useful for automated diagnosis of RBC deformation, but the system can be quite resource-intensive in case the CNN architecture is large. The proposed approach provides an empirical analysis of the application of 28 and 45-layer Binarized DenseNet for identifying RBC deformations. According to our investigation, the accuracy of the 45-layer binarized variant can reach 93–94%, which is on par with the results of the conventional variant, which also achieves 93–94% accuracy. The 23-layer binarized variant, while not on par with the regular variant, also gets very close in terms of accuracy. Meanwhile, the 45-layer and 28-layer binarized variant only requires 9% and 11% storage space respectively to that of regular DenseNet, with potentially faster inference time. This optimized model can be useful since it can be easily deployed in resource-constrained devices, such as mobile phones and cheap embedded systems.
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Reza, M.T., Dipto, S.M., Parvez, M.Z., Barua, P.D., Chakraborty, S. (2023). A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_21
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