Blood Cell Identification Using Deep Transfer Learning and Explainable Artificial Intelligence Techniques

Blood cell identification is a critical task in medical diagnosis, particularly for detecting and monitoring various hematological conditions. Automation of blood cell detection offers a solution to these challenges. This study presents a comprehensive study on the application of deep learning for automated blood cell identification, specifically, the Inception V3 model, showcasing its potential to revolutionize diagnostic accuracy in medical imaging. By employing advanced pre-processing techniques, and optimal model design, the InceptionV3 model achieved impressive results with 98.8% test accuracy on a dataset of high-quality microscopic blood cell images. To enhance decision-making transparency, the study employs Explainable AI (XAI) approaches such as Grad-CAM and Grad-CAM++ to produce heatmaps that highlight significant regions influencing the model’s identification. The study incorporated a comparative investigation with other deep-learning models, including VGG16, VGG19, and RESNET50. The efficacy of blood cell identification is demonstrated by evaluation measures like precision, recall, F1 score, Jaccard similarity, MCC score, and overall accuracy. The combination of strong performance and transparency through XAI techniques makes it a valuable tool for precise and dependable diagnoses. The potential of integrating advanced deep learning and XAI methods to improve the reliability and clinical adoption of automated blood cell identification systems is highlighted in this study.