Deep-Learning based Blood Cells Classification and Initial Edge Device Implementation
Md. Raisul Islam, Yannick Le Moullec, Fariha Afrin, Faisal Ahmed · 2022
The suitability of two deep learning models for classifying microscopic medical images is investigated on four types of white blood cell images; the inference phase is imple-mented onto a single board computer. The first proposed model is custom-developed by careful selection of the hyperparameters; the second one is based on transfer learning. A dataset of 12500 images is used. For the custom model, the training and validation accuracies are 99% and 97%, respectively, with overall classification accuracy of 97.77%. For the transferred model, the training and validation accuracies are 92% and 87%, respectively, with overall classification accuracy of 92% The two models are minimized and deployed on an RPi4 (quad-core ARM Cortex-A72 processor) for classification inference; the overall accuracy and classification times are 98.54% & 48.2 ms for the custom model vs. 91.3% & 142 ms for the transferred model, confirming that the custom model outperforms the transferred one.