Automated Blood Cell Identification, Counting, and Sub type classification using Deep Learning

Accurate identification and quantification of blood cells, such as platelets, white blood cells (WBCs), and red blood cells (RBCs), are of the utmost importance in medical diagnostics. Conventional approaches depend on manual procedures or automated analyzers; nevertheless, recent progressions in deep learning present more efficient substitutes. This study introduces a holistic methodology for blood cell analysis through the utilization of sophisticated neural network-based structures. These structures are designed to identify, quantify, and classify blood cells according to their subtypes. The BCCD dataset is utilized to enhance precision and surmount challenges such as overlapping cells and low resolution through the implementation of various preprocessing techniques. Blood cell identification by our research was accomplished with an exceptional 90% precision by employing Convolutional Neural Networks (CNN) utilizing the MXNet architecture. Moreover, the accuracy achieved by the VGG16 architecture integrated with Keras in the sub-classification of WBCs was 85%.The results of the experiment demonstrate the efficacy of the proposed methodology, indicating a positive potential for strengthening diagnostic accuracy and efficiency in clinical practice, ultimately improving patient care standards.