Deep Learning Segmentation and Classification of Red Blood Cells Using a
  Large Multi-Scanner Dataset
Mohamed Elmanna Ahmed Elsafty Yomna Ahmed Muhammad Rushdi Ahmed Morsy
Digital pathology has recently been revolutionized by advancements in
artificial intelligence, deep learning, and high-performance computing. With
its advanced tools, digital pathology can help improve and speed up the
diagnostic process, reduce human errors, and streamline the reporting step. In
this paper, we report a new large red blood cell (RBC) image dataset and
propose a two-stage deep learning framework for RBC image segmentation and
classification. The dataset is a highly diverse dataset of more than 100K RBCs
containing eight different classes. The dataset, which is considerably larger
than any publicly available hematopathology dataset, was labeled independently
by two hematopathologists who also manually created masks for RBC cell
segmentation. Subsequently, in the proposed framework, first, a U-Net model was
trained to achieve automatic RBC image segmentation. Second, an EfficientNetB0
model was trained to classify RBC images into one of the eight classes using a
transfer learning approach with a 5X2 cross-validation scheme. An IoU of 98.03%
and an average classification accuracy of 96.5% were attained on the test set.
Moreover, we have performed experimental comparisons against several prominent
CNN models. These comparisons show the superiority of the proposed model with a
good balance between performance and computational cost.