Deep Learning Models for Classification of Red Blood Cells in Microscopy Images for Anemia Diagnosis
Navid T. Zaman, Md. Farhad Billah, Kazi Ehsanul Haque, Monir Morshed, K. M. Safin Kamal, Monir Morshed, Ahmed Wasif Reza, Mohammad Shamsul Arefin · 2024
Anemia, a condition affecting human red blood cells (RBCs), presents in various forms. Different blood cell types and anemia variants exist. Addressing such a significant challenge requires integrating pathophysiology, advanced technology, and a comprehensive understanding of RBC classifications. In this pursuit, we utilize Deep Learning (DL) models to establish connections and propose innovative solutions to pathophysiological issues related to anaemia diagnosis through RBC classification. The customized Convolutional Neural Network (CNN) demonstrates exceptional performance, boasting a Training Accuracy of 99.42% and a Test Accuracy of 98.88%. Furthermore, the Training Loss is impressively low at 0.0232, while the Validation Loss remains minimal at 0.0964. The associated confusion matrix attests to the model's robust performance, affirming its accuracy in classification tasks.