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.