Morphology of Red Blood Cells Classification using Deep Learning Approach

Poikilocytosis is a condition in which the shape of RBC cells changes regularly. This condition is arising from several anemias and other diseases. It is critical to detect and classify abnormal RBC shapes as early as possible to diagnose and treat the condition. The proposed four different deep Convolutional Neural Networks (CNNs) are for RBC morphology classification, which is beneficial to hematologists. After using the data augmentation strategy, deep CNN produces good results and improves the performance of the models. The proposed deep CNN is tested on the recently available Erythrocytes IDB datasets. In Erythrocytes, there are three versions of IDB datasets IDB 1, IDB 2, and IDB 3. The proposed deep CNN models obtained an accuracy of 97.77 %, 96.28 %, 96.10 %, and 95.95 % and outperformed existing benchmark methods. The proposed CNN models produce good accuracy, speed, and classification rate results.